Finance Data Engineer (Consultant)

Overview

In this role you contribute to Finance Reinvention by building and governing the finance data and ontology layer used by agentic solutions. You will transform live ERP and EPM data into trusted, auditable data products and semantic models that support AI, analytics and decision making. You’ll collaborate with finance SMEs and cross-functional teams to enable secure, observable, and scalable data services. The position blends deep data engineering with finance-domain insight to drive measurable business impact and CFO-focused outcomes. This opportunity offers exposure to senior decision-makers and a chance to shape enterprise-wide data products and controls.

Responsibilities

• Expand and maintain the finance ontology, including entities, relationships, vocabularies, hierarchies, ownership and lifecycle versioning
• Ingest data from SAP, Oracle, Workday and EPM platforms into governed data products with support for structured/unstructured sources, batch and streaming patterns
• Implement entity-resolution and master/reference data capabilities to reconcile identifiers across source systems
• Establish data lineage and audit-ready evidence with data-quality checks, reconciliations, completeness and freshness metrics, observability and recoverable operations
• Define data contracts between delivery pods and client platforms, including schemas, quality thresholds, access rules, SLAs, versioning and change control
• Leverage cloud platforms (Azure/AWS/GCP) and modern data tools (Databricks, Snowflake, Palantir, etc.) with containers, Kubernetes or serverless patterns
• Collaborate with AI engineers on retrieval/grounding, context scoping, embeddings, and permission-aware filtering
• Apply security and privacy controls across the data lifecycle, including access controls, masking, retention and audit logging
• Adopt DataOps practices (Git, CI/CD, IaC, testing, performance tuning, cost optimization) and provide production support
• Work with finance SMEs and architects to translate business definitions into implementable data products and acceptance criteria

Key requirements

• Production data engineering with SQL and Python
• Experience with Databricks, Snowflake or Fabric and ETL/ELT, data testing, Git and CI/CD; PySpark or dbt is beneficial
• Knowledge of finance data structures (chart of accounts, entity hierarchy, intercompany, period close, actual/budget/forecast, currencies)
• Strong data modeling and ability to define semantic models, data contracts, schema evolution, metadata, lineage and data quality controls
• Experience extracting/reconciling data from enterprise applications via APIs, files, databases or CDC
• Experience deploying software/data products on Azure/AWS/GCP with cloud services and at least one of containers, Kubernetes or serverless
• Understanding of secure data engineering including access control, encryption, privacy, retention and handling of sensitive data
• Ability to translate business definitions into data products and acceptance criteria
• At least 4 years’ relevant professional experience
• Desirable: knowledge of data quality, MDM or lineage tooling; SOX/GDPR/regulatory awareness; finance-platform data models

• collaboration with cross-functional teams
• attention to governance and controls
• problem solving and strategic thinking
• SQL
• Python
• Databricks

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