Finance Data Engineer; Senior Manager

Finance is one of the most demanding and valuable environments in which to apply modern technology. You will work with complex enterprise data, mission‑critical processes and high‑impact decisions, using AI, data and engineering to reshape how organisations plan, control performance and allocate resources. The opportunity goes beyond building technically strong solutions: you will see how those solutions influence cash, profitability, risk and business growth, and take them from experimentation into trusted, production‑ready capabilities.

Working in Finance Reinvention allows you to remain close to leading‑edge technology while developing an understanding of the CFO agenda, gaining exposure to senior decision‑makers and building the commercial judgement needed to solve enterprise‑wide challenges. This combination of deep technical capability, finance‑domain expertise and measurable business impact creates a differentiated career path that is difficult to develop in a purely technology‑focused role.

Responsible for the finance data and ontology layer on which agentic solutions depend. Builds the entities, relationships, definitions, lineage and data contracts sourced from live ERP and EPM systems to a standard that supports controlled and auditable agent behaviour. The role owns governed finance data products, semantic models and retrieval‑ready data services that are secure, permission‑aware, observable and suitable for AI, analytics and decision intelligence.

• Build and extend the finance ontology covering supplier, invoice, customer, receivable, journal, account, cost centre and forecast line, and the relationships between them, including controlled vocabularies, business glossaries, hierarchy history, ownership and lifecycle versioning.

• Engineer pipelines from SAP, Oracle, Workday and EPM platforms into governed data products, supporting structured and unstructured sources, batch and incremental ingestion, APIs, files and change‑data‑capture patterns as appropriate.

• Build entity‑resolution and master or reference‑data capabilities that reconcile identifiers, definitions and hierarchies across source systems and preserve source‑to‑target mappings.

• Establish lineage and evidence sufficient for audit, so that agent output can be traced to source, with automated data‑quality checks, finance reconciliations, completeness and freshness measures, observability, alerts and recoverable operations.

• Define data contracts between delivery pods, and between Accenture delivery and client platform teams, including schemas, quality thresholds, access rules, service levels, versioning and change‑control expectations.

• Experience deploying software or data products on Azure, AWS or GCP, Data Bricks, Snowflake, Palantir etc using relevant cloud services and at least one of containers, Kubernetes or serverless patterns.

• Work with AI Engineers on retrieval and grounding, ensuring models receive appropriately scoped context, including chunking and embedding strategies, hybrid or graph retrieval, permission‑aware filtering, provenance and retrieval evaluation.

• Implement security and privacy controls across the data lifecycle, including role‑or‑attribute‑based access, row or document‑level permissions, masking or tokenisation, retention, tenant isolation and audit logging.

• Apply software and Data Ops practices including Git, automated testing, CI/CD, infrastructure as code where relevant, performance tuning, cost optimisation and production support.

• Collaborate with finance SMEs, data owners, architects and governance teams to agree definitions, resolve data issues and embed sustainable stewardship and operating ownership.

• Data…