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Data Conversion Lead - Record to report, Procurement, and Order to Cash

Job in City of Albany, Albany, Albany County, New York, 12201, USA
Listing for: Capgemini
Full Time position
Listed on 2026-10-18
Job specializations:
  • IT/Tech
    SAP Consultant, Data Analyst, IT Business Analyst
Salary/Wage Range or Industry Benchmark: 63000 - 99000 USD Yearly USD 63000.00 99000.00 YEAR
Job Description & How to Apply Below
Location: City of Albany

Data Conversion Lead - Record to report, Procurement, and Order to Cash (Contract)

Albany, NY, United States (Hybrid)

Contract (2 months 3 days)

Published 2 days ago

data governance

SQL

Oracle Migration Tools

data cleansing

data mapping

oracle fusion cloud

We are seeking an experienced Conversion Lead to own data conversion for the Finance, Procurement, and Order to Cash work streams of Project Fusion, a multi-phase Oracle Fusion Cloud ERP deployment. This role specializes in the Record to Report (Finance), Procurement, and Order to Cash process areas, including but not limited to General Ledger (GL), Fixed Assets (FA), Projects, Accounts Payable (AP), Purchasing (PO), Accounts Receivable (AR), and Order Management (OM).

The Conversion Lead reviews the functional and technical designs our System Integrator (SI) partner prepares, sets the guardrails and best practices that govern conversion, and coordinates the business owners, data cleanse teams, and technical stakeholders who deliver clean, reconciled data across every mock load and the final cutover. The ideal candidate combines deep finance, procurement and supply chain data knowledge with hands‑on Oracle Cloud migration experience and strong leadership skills.

Key Responsibilities:

Conversion Strategy, Guardrails, and Best Practices:

Define the conversion approach, standards, and guardrails for all in-scope modules, including data ownership, load sequencing, entry and exit criteria for each mock load, and go/no-go criteria for cutover.

Establish best practices for cutover cut‑off dates, open‑item versus historical data scope, balance conversion, and the treatment of inactive, duplicate, and obsolete records.

Set data quality thresholds and sign‑off standards, and enforce them across the SI team, business owners, and data cleanse teams.

Maintain a conversion risk and issue log, and elevate dependencies and risks to project leadership early.

Review of SI Functional and Technical Designs:

Review and challenge the functional design documents, technical design documents, field-level mapping specifications, and load templates the SI partner prepares for each conversion object.

Confirm that designs align with the Fusion configuration, including but not limited to ledgers, chart of accounts, business units, legal entities, and module-specific setup.

Verify that transformation rules, default values, validations, load sequencing, and error handling meet the guardrails we define, and that the designs support repeatable mock loads.

Approve or return designs with documented findings, and track remediation to closure.

Module-Specific Conversion Ownership:

Own end-to-end conversion for the in-scope modules, including but not limited to:

General Ledger (GL): balances, chart of accounts mappings, and journals.

Fixed Assets (FA): asset registers and depreciation balances.

Projects: project structures, budgets, and open balances.

Accounts Payable (AP): suppliers and open payables.

Purchasing (PO): open purchase orders and agreements.

Accounts Receivable (AR): customers and open receivables.

Order Management (OM): open sales orders and agreements.

Manage cross-module dependencies and load sequencing, including supplier and customer masters, ledger setup, and Projects, so that each load completes in the correct order.

Data Cleanse and Business Coordination:

Lead the data cleanse strategy for Finance, Procurement, and Order to Cash data, including duplicate supplier and customer consolidation, address and tax standardization, bank account validation, and retirement of inactive records.

Work with business data owners and data cleanse teams to assign owners, set cleanse deadlines, and resolve discrepancies identified in the legacy data.

Define data standards and governance policies that…

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