Synthetic Dataset & Generator for Waste Hauling Company

$1500-3000 USD

Closed

Posted 7 days ago

$1500-3000 USD

Paid on delivery

Category tags: Intuit QuickBooks · Accounting · Python · API Integration · Financial Analysis · Data Management · SQL Budget: Fixed price, milestone-based. Bid the full scope. Timeline: 3–4 weeks from kickoff. What I need A reproducible synthetic dataset for a fictitious solid-waste hauling company, covering 24 consecutive months of operations. This is for internal staff training, reporting/BI sandbox testing, and month-end close practice. No real company, customer, vendor, or employee data will be used or accepted. The scope has two linked layers: Financial layer — a populated QuickBooks company file (GL, AR, AP, payroll, fixed assets). Operational layer — the underlying service, tonnage, container, and route data that the financials are built from. The two must reconcile to each other on shared keys. This is not optional or a nice-to-have — see "Operational layer" below. A GL-only dataset will not be accepted. The other critical requirement: I want a generator, not just a filled-in file. I need to be able to re-run it, extend the period, or change parameters without re-hiring. A hand-keyed file is not an acceptable deliverable. The fictitious company Single US operating entity, one state, accrual basis, calendar fiscal year. Annual revenue target: ~$8M in year 1, growing ~11% in year 2. Service lines and revenue mix (approximate, you can propose adjustments): Commercial front-load — recurring monthly subscription billing Roll-off / temporary — per-haul charge plus per-ton disposal Residential subscription — quarterly prepaid (creates deferred revenue) Ancillary: fuel surcharge, environmental/regulatory fee, container delivery, extra pickups, contamination/overweight charges, late fees Customer base of roughly 150–250 accounts with a realistic concentration curve (top 10 accounts = meaningful share of revenue), plus new-customer adds and churn across the 24 months. Chart of accounts and structure Full COA separating Revenue, COGS/Direct Cost, Opex, Assets, Liabilities, Equity. Direct costs must include: disposal / tipping fees, transfer station charges, fuel, driver wages and overtime, subcontracted hauling, truck maintenance and parts, tires, container repair. Fixed assets: front-load trucks, roll-off trucks, rear-loaders, containers, compactors — each with acquisition date, cost, useful life, and monthly depreciation posted. Liabilities: equipment finance notes with amortizing principal/interest splits, accrued payroll, accrued disposal, deferred revenue. Classes or locations for at least two operating branches/routes, so segment reporting is testable. Realism requirements (this is where most bids fall short) Seasonality — roll-off and construction-driven volume peaks in warm months; Q1 trough. Should be visible in monthly revenue and disposal cost. Cost correlation — disposal cost must move with tonnage/volume, not float independently. Fuel cost should track a drifting synthetic price series. Price actions — annual rate increases applied at customer anniversary dates, not all on 1/1. AR behavior — an aging distribution with a genuine >90-day tail, partial payments, credit memos, customer disputes, unapplied cash, and a small number of bad-debt write-offs. AP behavior — vendor bills, partial payments, a handful of vendor credits. Payroll — drivers, helpers, dispatch, mechanics, sales, admin. Overtime concentrated in peak months. Controlled defects with an answer key. I want a defined set of intentional errors seeded into the data: misclassified expenses, a duplicate vendor bill, a transaction posted to the wrong period, a mis-keyed customer, an unreconciled bank item. These must be documented in a separate defect ledger so I can grade trainees against it. Tell me in your bid how many defects you'd seed and how you'd document them. Operational layer (must reconcile to the GL) QuickBooks alone cannot represent how this industry actually works. Revenue and disposal cost in a hauling business are driven by service events, tonnage, and container logistics — and a large part of my training goal is teaching people that bridge (tons → disposal cost → gross margin per route). So the GL must be generated from an operational dataset, not invented alongside one. Deliver the following as flat files (CSV, or SQLite if you prefer), keyed so they join cleanly to each other and to the QuickBooks records: Table Grain Key fields Route master One row per route route_id, branch, service days, assigned truck Truck / asset register One row per vehicle truck_id, type, route_id, in-service date, ties to GL fixed assets Container register One row per container container_id, size, type, status (on-site / yard / repair), assigned customer Container moves One row per delivery, swap, or pull container_id, customer_id, date, move type Service events One row per lift or pull event_id, date, customer_id, service_location, route_id, truck_id, service type, container size, billable flag Disposal tickets One row per landfill/transfer scale ticket ticket_id, date, truck_id, facility, gross/tare/net tons, rate per ton Fuel log Truck by week truck_id, week, gallons, price per gallon Labor hours Employee by week employee_id, week, regular hours, OT hours, route_id Required reconciliations. Each of these must hold for every month, and you should demonstrate them in the tie-out pack. Tolerance is 1% of the monthly GL balance for the account in question — these do not need to tie to the penny, and I'd rather you not add artificial plug entries to force an exact match. The key mappings in the last bullet must be exact. Billable service events × contracted rate = recurring + per-haul revenue in the GL. Disposal tickets: net tons × facility rate = disposal/tipping expense in the GL. Fuel log: gallons × price = fuel expense in the GL. Labor hours × pay rate (incl. OT premium) = driver and helper wages in the GL. Active container assignments are consistent with active customer count and service events. customer_id in the operational tables maps 1:1 to the QuickBooks customer list; truck_id maps to the fixed asset register. Seasonality, growth, and churn must originate in the operational layer and flow through to the financials — not be applied to the GL independently. Operational defects. Extend the seeded-defect concept into this layer: e.g. a disposal ticket with no corresponding GL entry, a container still assigned to a churned customer, a service event billed twice, a route with a missing week of events. Document these in the same defect ledger. Each seeded defect with a dollar impact must exceed 2% of the affected account's monthly balance — large enough to be unambiguously distinguishable from the 1% reconciliation tolerance, so a trainee can actually find it. Deliverables Populated QuickBooks company file covering 24 months, with each month closed and a clean trial balance. Source data as CSVs — every customer, vendor, employee, item, invoice, bill, payment, and journal entry — plus the field mapping used to load them. The generator — script or parameterized workbook with a fixed random seed, so output is reproducible. Documented well enough that my team can change revenue size, period length, or customer count and re-run it. Operational layer files — all eight tables above, with an entity-relationship diagram showing how they join to each other and to the QuickBooks records. Reconciliation pack — a worksheet proving, month by month, that each of the required reconciliations above holds. This is the primary evidence I'll use to accept or reject the work. Synthetic bank statement CSV that reconciles to the cash account (with the seeded exception). Data dictionary (covering both layers) and defect ledger / answer key. Report pack at delivery for tie-out: monthly P&L, balance sheet, AR aging, AP aging, GL detail, fixed asset roll-forward, plus gross margin by route and cost per ton. Short handover notes. Acceptance criteria Trial balance balances exactly in every one of the 24 months. AR, AP, and fixed asset subledgers tie exactly to the GL. No negative or nonsensical account balances outside the seeded defects. Revenue mix and monthly seasonality fall within the bands agreed at Milestone 1. Every operational-to-GL reconciliation listed above holds in all 24 months within 1% of the monthly GL balance, with the seeded defects being the only exceptions. Operational and QuickBooks records join without orphan keys. Re-running the generator with the same seed reproduces the same dataset. Milestones Spec sign-off + COA + data model + one fully populated month across both layers — includes your generation approach, the ER diagram, and the defect list. I will not release sandbox access before this is accepted. Months 1–12 loaded and tied out. Months 13–24, generator, documentation, defect ledger, report pack. Environment and security I will provide a dedicated, empty QuickBooks environment containing no real data and no connected bank feeds. Credentials are released at Milestone 1, not at award. Tell me in your bid which QuickBooks SKU you recommend for this transaction volume and why, and whether you'd load via API, import file, or manual entry. If you see a volume or API-throttle constraint at 24 months, say so — I'd rather hear it now. NDA and full IP assignment of the generator and all output. No use of real-world data from any source. How to bid Generic proposals restating this listing will be ignored. In your bid, answer these four: How will you generate and load the data, specifically? Name the tools. Roughly how many transactions do you expect across 24 months, and what's your throughput plan? How will you drive the GL from the operational tables so that tonnage, service events, and hours foot to disposal cost, revenue, and wages? Describe the flow, not the intent. Have you built synthetic accounting or operational data before? Describe one example. Please also confirm you're bidding on 24 months, not 2 or 12.

Project ID: 40703709

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91 freelancers are bidding on average $2,108 USD for this job

@AwaisChaudhry

I’m bidding on the complete 24-month scope, with the operational layer driving the accounting, not a GL-only file. I would build a seeded Python generator using pandas, NumPy, Faker, and SQLite/PostgreSQL staging tables. It will create customers, contracts, routes, service events, disposal tickets, fuel, labor, assets, subledgers, defects, and bank activity from shared keys. SQL validation views will enforce orphan-key checks, monthly tie-outs, and the 1% tolerance. API Integration would use the QuickBooks Online Advanced SKU, which is better suited to this transaction volume and audit/reporting needs; I would load through the QuickBooks Online API in batched, rate-aware queues, with CSV import fallback for supported entities. I would not use manual entry. I’ll document API throttling and retain replayable payloads. For 24 months, I estimate approximately 45,000-75,000 operational and accounting records, depending on service frequency and invoice granularity. The throughput plan is staged: master data, operational events, derived subledgers, journalized GL, then API batches with checkpointing and post-load reconciliation. The flow is explicit: billable events × contracted rates produce invoices and revenue; net tons × facility rates produce disposal bills/COGS; fuel gallons × weekly prices produce fuel expense; labor hours × pay rules produce wages and overtime. These generated postings retain source IDs, allowing Financial Analysis and Data Management tie-outs by month an

$3,000 USD in 16 days

4.9

(49 reviews)

8.4

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Synthetic Dataset for a Solid-Waste Hauling Company ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project details and see you are looking for a synthetic dataset for a solid-waste hauling company. Look no further; Zohaib is here to assist you! My team has completed over 50 similar projects in data generation. I will create a dynamic generator for your dataset, ensuring it meets all your specifications and can be easily reused. ➡️ Why Me? I have 5 years of experience in data management and financial modeling, particularly in creating synthetic datasets. My skills include Python programming, SQL, and API integration, giving me a strong grip on building efficient solutions for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Data Generation ✅ Python Programming ✅ SQL Database Management ✅ API Integration ✅ Financial Modeling ✅ Data Reconciliation ✅ Data Validation ✅ Reporting & BI ✅ Data Manipulation ✅ Error Handling ✅ Project Documentation ✅ Time Management Waiting for your response! Best Regards, Zohaib

$1,800 USD in 2 days

4.9

(128 reviews)

7.9

@diektech

Hi, This is Elias from Miami. I’ve checked the full scope, and I understand the key point: the QuickBooks financial layer must be generated from the operational data, with both layers joining cleanly and reconciling throughout all 24 months. I’d build this with Python, Pandas, Faker, SQLite/CSV, SQL validation queries, and the QuickBooks Online API. I’d recommend QuickBooks Online Advanced because of the transaction volume and reporting needs. I’d use API-based loading rather than manual entry, with batching, retries, throttling controls, and validation after each period. Q1: I’d expect roughly 30k–60k records across invoices, bills, payments, payroll, service events, disposal tickets, fuel, labor, journals, and related tables. I’d generate in batches, validate locally first, then load month-by-month with reconciliation checkpoints. Q2: The operational layer will be the source of truth: service events drive billable revenue, disposal tickets drive tonnage × facility rate, fuel logs drive gallons × price, and labor hours drive wages/OT. Those calculated amounts will feed the corresponding GL transactions, preserving customer/route/truck keys. Q3: I’d seed around 15–20 controlled defects across both layers, including duplicate billing, missing GL linkage, wrong-period posting, customer/container issues, and bank exceptions. Each will be documented with location, expected result, dollar impact, and correction in the defect ledger. Regards.

$2,250 USD in 7 days

4.9

(12 reviews)

7.6

@Hubready

Hello!, I am a US-based full stack/data developer with around 10 years of experience building accounting data pipelines, BI-ready datasets, Python generators, SQL models, API integrations, and reconciliation/reporting workflows. I read your project carefully and confirm I’m bidding on the full 24 months, not 2 or 12. I understand the key requirement: the QuickBooks GL cannot be invented separately. The operational layer must drive revenue, disposal, fuel, labor, assets, AR/AP, defects, and the reconciliation pack. My approach would be Python + Pandas/Faker + SQLite/CSV as the generator layer, PostgreSQL-style relational modeling for validation, and QuickBooks Online Advanced as the recommended SKU unless volume testing shows Enterprise is safer. I’d load via API where possible, with import files as fallback for high-volume objects. I’d expect roughly 25k–60k transactions across 24 months depending customer/event density. Throughput plan: generate operational events first, calculate billing/cost/payroll/depreciation from shared keys, create AR/AP/JE/payment records, validate monthly tie-outs, then load in controlled batches. Could you please clarify the following questions to help me better understand the project? 1. Is QuickBooks Online preferred, or is Desktop/Enterprise acceptable? 2. Do you want seeded defects spread evenly by month or concentrated near close periods? 3. Should payroll be summarized by week or loaded at employee-level detail? Regards, James Zappi

$2,250 USD in 7 days

5.0

(8 reviews)

6.5

@techplusintl

★★★ PYTHON SPECIALIST ★★★ Hi, To generate a reproducible synthetic dataset for the solid-waste hauling company, I can create a robust data generation script that will populate both the financial and operational layers while ensuring they reconcile accurately. I will implement a generator that allows for parameter adjustments, ensuring flexibility for future needs. The operational layer will include detailed service events, disposal tickets, and labor hours, all tied to the financial layer through shared keys. I will also seed intentional defects in both layers, with a minimum of 10 defects documented in a separate ledger for training purposes. My technical approach involves using Python with libraries such as Pandas for data manipulation and SQLite for database management. I will ensure that the GL is driven by the operational data, maintaining the necessary correlations between service events, tonnage, and costs. I recommend using QuickBooks Online Plus for this transaction volume, as it supports the required features and can be loaded via API for efficiency. To get started, I will need access to the QuickBooks environment and any specific parameters you want for the dataset. Thanks!

$1,500 USD in 28 days

4.6

(37 reviews)

6.3

@HamparProd

I’m bidding on the full 24 months. I would generate the operational model first in Python using Pandas/NumPy plus SQLite for deterministic staging and validation. Service events, tonnage, fuel, labor, churn, seasonality and anniversary price actions would drive all downstream accounting. QBO transactions would never be generated independently. Billable events create invoice lines; disposal tickets × facility rate create disposal AP/expense; fuel logs drive fuel cost; employee hours × pay/OT rates drive wages/accruals. Every generated transaction retains source IDs so the monthly reconciliation pack can trace GL values back to operational rows. I expect roughly 12k–20k QBO transactions plus 40k–60k operational rows. I’d fully generate/reconcile locally, then load QuickBooks Online Advanced through the Accounting API in dependency-aware batches with retries/idempotency. I’d seed 16 documented defects: 10 financial and 6 operational, each recorded in an answer-key ledger with IDs, period, impact, expected result and detection method. I can start immediately after award.

$2,850 USD in 28 days

5.0

(4 reviews)

5.7

@AndrewJSMAN

I've built synthetic accounting datasets for training before, so this is right in my wheelhouse. I'll generate the operational layer first in Python using pandas, numpy, and Faker, then map it to QuickBooks via API (QBO v3) with a custom reconciliation engine to ensure 1% tolerance. For 24 months at ~$8M/year, expect ~250K transactions (invoices, bills, journal entries) with a batching strategy to avoid API throttling. The GL will be driven by aggregating service events (tons → disposal cost → revenue) and labor hours, with seasonality modeled via sinusoidal volume curves. I'll seed 5-7 defects per layer (e.g., duplicate bills, missing container assignments) in a SQLite defect ledger. I'll use QuickBooks Online Plus for the transaction volume and recommend API loading to maintain referential integrity. I can start immediately. Thanks, Andrii.

$2,250 USD in 30 days

5.0

(5 reviews)

5.3

@mayr81

As an experienced freelancer with a background in web and mobile development, AI automation, and project coordination, I am thrilled to offer my skillset to deliver a quality synthetic dataset and generator for your waste hauling company. My versatile skills can help provide a powerful dataset that accurately reflects the financial and operational layers you require while staying committed to the timeline and budget. Drawing from my proficiency in Python and SQL, I can build a synthetic dataset that fully mirrors the integrated Quickbooks company file structure along with the operational components of your waste hauling business. From AR aging distribution to controlled errors, each data point will be intentionally crafted following your specifications, ensuring a realistic experience for your staff to train on, test BI sandbox models or practice month-end closes. My clients appreciate my consistent focus on clear communication, reliability, detail-oriented work, fast turnaround time, a strong understanding of both technology and business operations – all qualities that align perfectly with your needs. Not only will I provide you with an analytically dense and comprehensive synthetic dataset throughout your entire service lines but also develop a generator for you to extend its period or change parameters without any extra cost. So let's leverage my automation expertise together!

$1,500 USD in 7 days

4.9

(7 reviews)

5.6

@gotucat

Hi, I reviewed the project and understand the requirement is a reproducible 24-month synthetic waste-hauling dataset where operational records reconcile to QuickBooks financials, with controlled defects and tie-out evidence. I’ll build the generator in Python with a fixed seed, using SQL/SQLite or CSV source tables, then drive revenue, disposal cost, fuel, and wages from service events, disposal tickets, fuel logs, and labor hours. I’ll load QuickBooks through API Integration where supported, using batch processing and import files where volume or API limits require it. I’ll recommend QuickBooks Online Advanced for transaction capacity and reporting, estimate the transaction volume after the model is approved, and seed/document defects across both layers in a defect ledger with an answer key. This bid covers all 24 months, including the ERD, mappings, reconciliations, reports, and handover documentation. I’ll provide clean, reproducible outputs, exact subledger tie-outs, monthly reconciliation evidence, and responsive milestone-based management. Let’s discuss here now.

$1,500 USD in 30 days

5.0

(11 reviews)

5.1

@sekai1116

I can build and deliver the full 24-month dataset, generator, QuickBooks company file, operational layer, reconciliation pack, reports, and documented defect ledger. I am bidding on all 24 months, not a partial period. My approach would use Python, Pandas, Faker, SQLite/CSV, and the QuickBooks API/import tooling. The generator will use a fixed random seed and create the operational layer first: customers, routes, trucks, containers, service events, disposal tickets, fuel, and labor. For approximately 150–250 customers across 24 months, I expect the dataset to contain tens of thousands of operational records and several thousand financial transactions. I’d generate in batches locally, validate the complete dataset, then load QuickBooks in controlled batches rather than relying on manual entry. I would recommend QuickBooks Online Advanced for this transaction-heavy training environment, with API/import loading and staged validation to manage volume and throttling. I’ll seed and document both financial and operational defects, with the dollar-impact requirement respected, and produce monthly reconciliation evidence rather than using artificial plugs. I can build the generator from scratch and provide the complete reproducible system, documentation, ERD, reports, and handover under the required IP assignment.

$2,000 USD in 7 days

5.0

(5 reviews)

4.9

@jonasdedzo

Hi there, I can build the complete 24-month synthetic accounting and operational dataset with a reproducible Python generator, QuickBooks integration, SQL/CSV operational layer, reconciliation pack, seeded defects, and reporting outputs. I’ll generate operational events first, then drive revenue, disposal costs, fuel, and labor into the GL from those records so both layers remain linked and auditable. I’ll use Python, Pandas/NumPy, SQL/SQLite, and QuickBooks API/import tools with batch processing and validation to handle the expected transaction volume efficiently. I’ll also use a fixed random seed so the dataset can be regenerated or extended consistently. For defects, I propose 8–10 intentional issues across both layers, documented in a clear answer-key ledger. I confirm the scope is the complete 24 months. M1: COA, data model, ERD + Month 1 — $500 | 4 days M2: Months 2–12 + reconciliation — $600 | 5 days M3: Months 13–24, generator, defects, reports + handover — $700 | 6 days Total: $1,800 | 15 days Looking forward to work with you. Thanks

$2,250 USD in 7 days

5.0

(2 reviews)

4.2

@Georgeoustar

Hi there, I’m excited by your need for a robust, reproducible synthetic dataset and generator for a waste hauling operation. I understand the core requirement: every financial record in QuickBooks must be driven by a reconciled operational event, ensuring realism and teaching the vital bridge from operations to financials. **1. Data Generation & Loading:** I’ll use Python (Pandas, Faker, NumPy) for synthetic data generation, driving both operational and financial layers from shared random seeds and parameter sets. Data will be exported as CSVs and loaded into QuickBooks Desktop via IIF import for maximum control and throughput, avoiding API throttling at this transaction volume. For this scale, I recommend QuickBooks Desktop Premier as it handles large lists and high transaction counts efficiently. **2. Volume & Throughput:** Expecting ~5,000–10,000 transactions monthly (service events, payroll, AP/AR, GL entries), I’ll batch-generate and validate all transactions before import, with automated tie-outs at each stage. **3. Flow from Ops to GL:** Operational tables (service events, tonnage, labor hours) feed directly into invoice, bill, and payroll record creation, ensuring every dollar in GL is traceable to a real-world activity. Seasonality, growth, and churn originate operationally and cascade to the financials. **4. Relevant Experience:** Yes, I’ve built synthetic accounting and ops data: for a logistics startup, I created a full-stack dataset (orders, routes, payroll, GL) for BI and training—reconciling every financial entry to operational roots, including seeded errors for trainee testing. I am bidding for the full 24 months, as specified. Best Regards! George

$1,500 USD in 20 days

5.0

(2 reviews)

4.0

@SkillCrafts

I understand you need a robust synthetic dataset for a waste hauling company's training and sandbox needs, mirroring the complexity of financial data similar to what would be managed in QuickBooks. My experience with API integrations and data generation for financial reporting makes me well-suited for this task. My approach will involve Python scripting to generate the core dataset. I'll leverage libraries like Pandas for data manipulation and potentially Faker for generating realistic, yet fictitious, company and customer details. For the financial layer, I'll structure transactions (invoices, payments, expenses) to simulate 24 months of operations, ensuring logical flow and accounting principles are followed. I'll generate a CSV output that can be easily ingested into QuickBooks or other reporting tools. To ensure the dataset precisely meets your needs, could you clarify if there are specific QuickBooks chart of accounts categories you'd like prioritized or a particular volume of transactions per month you're targeting? I'm confident I can deliver a high-quality, reproducible synthetic dataset within your timeline. Let's connect to discuss the specifics.

$2,340 USD in 21 days

5.0

(2 reviews)

3.9

@juanestebana17

Hello! I understand you need a reproducible 24-month synthetic waste-hauling dataset where the operational layer drives QuickBooks financials, not the other way around. I’ve spent the last several years solving exactly this type of problem with Python, SQL, API Integration, Financial Analysis, Intuit QuickBooks, and Microsoft Office workflows, building generator-based accounting datasets, reconciled operational models, and audit-ready tie-out packs for training and BI sandbox use. I’m ready to get started, and I’m confident we can make this project a success and deliver it to a high standard. I’m committed to ensuring everything is completed professionally, efficiently, and exactly according to your requirements. Best regards!

$2,000 USD in 7 days

5.0

(2 reviews)

3.6

@aprkdream

Hi, I am a Full Stack Developer with extensive experience in building and integrating complex systems, and I am excited about the opportunity to create a synthetic dataset and generator for your waste hauling company. My expertise in Python and API integration will enable me to develop a robust solution tailored to your specific needs. I can ensure that the synthetic data aligns with your operational requirements, facilitating accurate financial analysis and reporting. Key deliverables I can provide include: - Development of a synthetic dataset generator using Python. - Integration with Intuit QuickBooks for seamless accounting. - Implementation of APIs to ensure smooth data flow between systems. - Comprehensive testing to ensure data accuracy and reliability. I focus on clean, maintainable code and effective communication to deliver production-ready solutions that meet your expectations. I look forward to the possibility of collaborating on this project. Best regards, Anil Prajapati

$1,500 USD in 5 days

5.0

(3 reviews)

3.2

@ranjika08

The hard part here is not creating a QuickBooks file, it is making the operational layer drive the accounting so revenue, disposal, fuel, wages, and route margin all reconcile month by month. I’d build this as a reproducible Python-based generator with a fixed seed, using relational tables first and then loading QuickBooks from mapped outputs so the GL is a result of service events, tons, fuel, and labor rather than a separate invention. I would also seed 14–16 controlled defects, each logged in a defect ledger with table, record key, month, rule broken, expected impact, and trainee answer note. I’ve handled accounting data structures, synthetic test data generation, imports/mappings, and reconciliation-heavy datasets before, including cases where subledgers had to tie cleanly to GL and where operational drivers had to roll into financial reporting. For this job I’d recommend QuickBooks Online Advanced if API-based loading is preferred, or QuickBooks Desktop if you want higher-volume import flexibility with less API-throttle friction across 24 months. A few specifics I’d want to lock before Milestone 1: - Do you already prefer QBO vs Desktop, or would you like me to recommend one based on expected transaction count and import method? - Should the 2 branches behave as separate route economics models, or mostly the same with different customer mix/seasonality? - Do you want the tie-out pack delivered in Excel, or is SQLite + CSV + workbook summary better for your team?

$1,500 USD in 7 days

5.0

(1 review)

2.8

@XRProConnect

Hi, Aashiq (Ash) here from Cape Town, South Africa. This project instantly caught my eye, so I had to reach out. I see you’re looking for a synthetic dataset and generator for a waste hauling company that reconciles financial and operational data. You want it to be flexible enough to allow for various parameter changes in the future. I’ve worked on similar projects before, helping businesses create realistic financial simulations and operational datasets. I’m confident I can deliver the intricate balance between your operational and financial layers. Feel free to ask for samples of successful projects I’ve completed. Based on what you mentioned, here is how we would approach the project: - Create a robust generator using Python and SQL for data integrity. - Design financial and operational tables to ensure smooth reconciliation. - Implement intentional defects with clear documentation for training purposes. You can expect clear communication throughout the process, ensuring a seamless, user-focused solution optimized for performance. Best Regards, Aashiq

$2,700 USD in 7 days

5.0

(1 review)

3.0

@SyedTaha04

Hi, I can build your reproducible, dual-layer synthetic dataset and generator script for solid-waste hauling operations covering 24 full months. Python synthetic data engineering, QuickBooks API integration, and financial database architecture are my primary skills, specializing in operational-to-GL reconciliations, ETL pipelines, and controlled defect seeding. I will write a parameterized Python generator that builds the operational layer first, drives the GL through exact bottom-up mapping, seeds documented training defects, and populates QuickBooks Online via API alongside a full tie-out pack. I have engineered complex financial models, automated data pipelines, and database systems requiring exact subledger-to-GL alignment. Message me in the chat so we can review your COA preferences and sign off on Milestone 1.

$1,800 USD in 7 days

4.1

(7 reviews)

3.1

@Eliteinno

Ensuring your synthetic dataset is reproducible hinges on crafting an effective generator. Python, coupled with Pandas for data manipulation and SQLAlchemy to handle database interactions, would be integral to this task. For loading data into QuickBooks, I'd recommend the API route for its scalability, especially given the transaction volume. An API throttle could be a concern, but manageable with batch processing over the 24-month window. From a transaction perspective, anticipate roughly 30,000 entries over this period. The GL will be synchronized with operational tables by directly mapping service events to revenue streams using predefined rates per event type, ensuring tonnage and labor cost match the disposal and wage expenses. How many seeded defects do you envision for effective training, and should they span both financial and operational layers?

$1,500 USD in 30 days

5.0

(1 review)

1.6

@hassanmustafa01

Hello. The hardest part of this Synthetic Dataset & Generator project is ensuring full reconciliation between the operational layer and the QuickBooks financial layer across 24 months with realistic seasonality, churn, and seeded defects. Your requirement for a reproducible generator that allows parameter changes without re-hiring adds complexity beyond static data creation. I propose this approach: 1. Develop a Python-based generator script using Pandas and SQLAlchemy to create operational data tables and derive GL transactions, ensuring all keys join cleanly. First deliverable: a fully populated month 1 dataset with ER diagram and defect ledger. 2. Use QuickBooks Online Advanced SKU for API-based data loading via Intuit’s API, handling volume and throttling with batch processing. 3. Implement reconciliation logic in the generator to flow service events, tonnage, and labor hours into GL accounts, respecting pricing, seasonality, and churn. 4. Deliver CSV exports, QuickBooks file, reconciliation workbook, and documentation. I built a similar synthetic dataset for a logistics client, generating operational events and financials that reconciled monthly with seeded errors for audit training. This experience directly matches your risk of data integrity and reconciliation. Please share repo access or initial specs to start milestone 1. Best,

$1,800 USD in 12 days