Job DescriptionUKI Finance RPFinance Data EngineerThe practiceFinance 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.
Purpose of the roleResponsible 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.
Responsibilities- 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, DataBricks, 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 DataOps 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.
Essential experience- Production data engineering, including SQL, Python, a modern data platform such as Databricks, Snowflake or Fabric, and orchestration tooling, with practical experience of ETL/ELT, data testing, Git and CI/CD; PySpark or dbt experience is beneficial.
- Working knowledge of finance data structures, including chart of accounts, entity hierarchy, sub-ledger to ledger relationships, intercompany and period close, together with actual, budget and forecast data, currencies, consolidation and management hierarchies.
- Strong data modelling discipline and the ability to justify and maintain definitional standards, including canonical or semantic models, data contracts, schema evolution, metadata, lineage and data-quality controls.
- Experience extracting and reconciling data from enterprise applications or complex operational systems using APIs, files, database interfaces or change-data-capture patterns.
- Experience deploying software or data products on Azure, AWS or GCP, DataBricks, Snowflake, Palantir etc. using relevant cloud services and at least one of containers, Kubernetes or serverless patterns. (AI engineer - desirable)
- Understanding of secure data engineering, including access control, encryption, secrets, privacy, retention and the handling of sensitive or regulated data.
- Ability to work with finance SMEs and technical teams to translate business definitions and controls into implementable data products and acceptance criteria.
- At least 10 years’ relevant professional experience
Desirable- Knowledge graph, semantic layer or ontology engineering experience, including technologies or standards such as Neo4j, RDF/OWL, SKOS, SPARQL or GraphRAG.
- Data quality, master data management or lineage tooling experience, including entity resolution, business glossaries, catalogues or platforms such as Collibra, Purview or Unity Catalog.
- Exposure to SOX or external audit requirements, GDPR, model-risk controls or regulated-data environments.
- Detailed knowledge of one or more finance-platform data models, such as SAP S/4HANA, Oracle Fusion, Workday Financials, Anaplan, OneStream or SAP Analytics Cloud.
- Experience evaluating retrieval quality for AI workloads, including embeddings, vector stores, hybrid search or reranking.
QualificationN/A
LocationsLondon
Manchester
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