Finance Data Engineer (Senior Manager)

In this role you shape the finance data layer used by AI-enabled decision systems, building governed data products and ontologies from ERP and EPM sources. You work closely with cross-functional teams to enable auditable, secure data services that support analytics and decision intelligence. You’ll translate finance concepts into scalable data products while aligning with the CFO agenda and enterprise governance. This role offers exposure to senior stakeholders and opportunities to impact cash, profitability and risk through production-ready solutions.

• Extend and maintain the finance ontology (suppliers, invoices, customers, receivables, journals, accounts, cost centres, forecast lines) and related vocabularies and hierarchies.
• Build and deploy data pipelines from SAP, Oracle, Workday and EPM into governed data products with support for structured/unstructured data, batch and incremental ingestion, APIs and change-data-capture.
• Implement entity-resolution and master/reference data capabilities to reconcile identifiers and maintain source-to-target mappings.
• Establish data lineage and evidence for audit, with automated quality checks, reconciliations, completeness and freshness metrics, observability and recoverable operations.
• Define data contracts between delivery pods and client/platform teams, including schemas, quality thresholds, access rules, SLAs, versioning and change-control expectations.
• Leverage cloud platforms (Azure/AWS/GCP), DataBricks, Snowflake, Palantir and related services, plus containers/Kubernetes/serverless patterns.
• Collaborate with AI engineers on retrieval/grounding, ensuring appropriate context, chunking/embedding strategies, permission-aware filtering and provenance.
• Apply data security and privacy controls across the lifecycle (RBAC, masking/tokenisation, retention, tenant isolation, audit logging).
• Adopt Software/DataOps practices (Git, automated testing, CI/CD, IaC, performance tuning, cost optimization, production support).
• Work with finance SMEs, data owners and governance teams to define, resolve data issues and establish stewardship and ownership.

• Production data engineering with SQL and Python
• Experience with modern data platforms (Databricks, Snowflake, Fabric) and orchestration tooling
• ETL/ELT, data testing, Git and CI/CD; PySpark or dbt is beneficial
• Strong knowledge of finance data structures (COA, hierarchies, intercompany, period close, currencies, consolidation)
• Data modeling discipline with semantic models, data contracts, metadata, lineage and data-quality controls
• Experience extracting/reconciling data from enterprise apps via APIs/files/DB interfaces/DC patterns
• Cloud deployment experience (Azure/AWS/GCP) with DataBricks, Snowflake, Palantir; container/Kubernetes/serverless patterns
• Understanding of secure data engineering (access control, encryption, privacy, retention, audit)
• Ability to translate business definitions into data products and acceptance criteria
• Minimum 10 years of relevant professional experience

• Collaborative stakeholder engagement
• Ability to translate business requirements into technical solutions
• Attention to data quality and governance
• SQL
• Python
• Databricks