Finance Data Engineer (Senior Manager)

Overview

In this Finance Data Engineer role, you will own the finance data and ontology layer that underpins AI-enabled, auditable agent behavior across ERP and EPM sources. You’ll transform live financial data into governed, retrieval-ready data products with secure access and traceable lineage. You’ll work closely with finance SMEs, governance teams and AI engineers to push data products from experimentation to production. This is a unique blend of technical execution, finance-domain insight and measurable business impact at scale.

Responsibilities

• Extend and maintain the finance ontology (supplier, invoice, customer, receivable, journal, account, cost centre, forecast line) including vocabularies, hierarchies and lifecycle/versioning
• Build data pipelines from SAP, Oracle, Workday and EPM platforms into governed data products with varied data sources and ingestion patterns
• Develop entity-resolution and master/reference data capabilities ensuring source-to-target mappings
• Establish data lineage and evidence for audit, implement data quality checks, reconciliations, and observability
• Define data contracts between delivery pods and client platforms, including schemas, access rules and SLAs
• Leverage cloud platforms (Azure, AWS, GCP) and data platforms (Databricks, Snowflake, Palantir) with container/Kubernetes/serverless patterns
• Collaborate with AI engineers on retrieval/grounding, including context scoping, embedding strategies and provenance
• Implement security and privacy controls (RBAC, masking, retention, tenant isolation, audit logging)
• Apply DataOps practices (Git, CI/CD, IaC, testing, performance tuning, cost optimization)
• Partner with finance SMEs and governance teams to resolve data issues and sustain ownership

Key requirements

• Production data engineering experience with SQL, Python, Databricks/Snowflake/Fabric
• ETL/ELT, data testing, Git, CI/CD, and orchestration tooling
• Knowledge of finance data structures and consolidation processes
• Strong data modelling discipline with semantic models, schemas and data quality controls
• Experience extracting data from enterprise systems via APIs, files or CDC
• Security-focused data engineering including access control and privacy measures
• 10+ years of relevant experience
• Knowledge of finance platforms like SAP S/4HANA, Oracle Fusion, Workday, Anaplan, OneStream or SAP Analytics Cloud
• Experience evaluating AI retrieval quality (embeddings, vector stores, hybrid search)
• Familiarity with SOX, GDPR or regulated data environments
• Knowledge graph or ontology engineering (Neo4j, RDF/OWL, SKOS, SPARQL)
• Experience with data governance tools (Collibra, Purview, Unity Catalog)
• collaboration with cross-functional teams
• stakeholder communication
• attention to data quality and governance
• SQL
• Python
• Databricks

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