Financial Data Business Analyst

Role: Financial Data Business Analyst Location: remote Type: Hybrid Experience: 5–9 years (Business Analysis + Finance Data + Data Engineering) Role Overview We are looking for a rare hybrid professional who bridges the gap between Finance domain expertise and modern data engineering practice. As a Financial Data Business Analyst / Functional Engineer, you will serve as the connective tissue between Finance stakeholders and data platform teams — translating complex business requirements into precise data models, functional specifications, and engineeringready designs. You will own the end-to-end lifecycle of financial data assets: from understanding source systems and business rules, to designing dimensional models and defining transformation logic, to validating that what gets built matches what the business actually needs. You are equally comfortable in a CFO’s strategy session and a data architect’s schema review. Key Responsibilities 1. Financial Domain & Stakeholder Engagement • Engage with Finance stakeholders (Controllers, FP&A, Treasury, Risk) to elicit, document, and validate data requirements • Translate business concepts — P&L structures, chart of accounts, cost center hierarchies, budget vs. actuals frameworks — into data definitions and lineage • Author Business Requirements Documents (BRDs), Functional Requirements Documents (FRDs), and data dictionaries with precision and business context • Define and document KPIs, metrics formulas, and business rules that govern financial reporting • Lead data discovery workshops and drive sign-off from Finance SMEs on data definitions 1. Data Modelling & Architecture • Design logical and physical data models for financial datasets — General Ledger, Trial Balance, Accounts Payable/Receivable, Cost Accounting, Revenue Recognition • Build dimensional models (star/snowflake schemas) optimized for financial analytics workloads • Define entity-relationship diagrams, data flow diagrams, and source-to-target mappings • Enforce data modelling standards, naming conventions, and governance policies across the data platform • Collaborate with Data Architects to ensure financial models align with enterprise data model and master data strategy 1. Data Engineering Collaboration & Functional Oversight • Produce detailed functional specifications for data pipelines, ETL/ELT transformations, and aggregation logic • Collaborate closely with data engineers to review and validate pipeline implementations against business rules • Write and review SQL for data validation, business logic verification, and analytical queries • Define data quality rules, reconciliation checks, and acceptance criteria for financial data loads • Participate in data model reviews, sprint planning, and backlog grooming within an Agile delivery framework 1. Data Governance & Quality • Champion data governance practices: ownership assignment, data lineage documentation, glossary management • Define and maintain business glossaries and metadata for financial data assets • Coordinate with Data Governance teams on regulatory compliance requirements (IFRS, SOX, Basel III where applicable) • Establish data quality SLAs and own issue resolution with upstream source system teams 1. Reporting & Analytics Enablement • Work with BI and Analytics teams to design semantic layers and reporting models for financial dashboards • Validate financial reports and dashboards against source-of-truth data; own UAT sign-off • Define aggregation hierarchies (legal entity, cost center, product line, time dimension) for management reporting • Support self-service analytics by producing clear data model documentation consumable by business users Required Qualifications Finance Domain Knowledge • 5+ years working with financial data in enterprise environments (ERP, GL, finance reporting) • Deep understanding of core finance concepts: Chart of Accounts, General Ledger, P&L, Balance Sheet, Cash Flow, Intercompany eliminations • Familiarity with financial close processes, period-end reporting cycles, and reconciliation workflows • Exposure to financial systems: SAP (FI/CO modules), Oracle Financials, Workday Finance, or equivalent • Working knowledge of accounting standards relevant to data: IFRS 15/16, US GAAP revenue recognition, or SOX controls Business Analysis • Proven ability to produce high-quality FRDs, BRDs, data dictionaries, and source-to-target mapping documents • Strong stakeholder facilitation skills — ability to run workshops with mixed technical and nontechnical audiences • Proficiency in process modelling (BPMN), use case documentation, and user story authoring • Experience working in Agile/Scrum delivery environments with cross-functional squads Data Engineering & Modelling • Solid understanding of data warehousing concepts: Kimball/Inmon methodology, dimensional modelling, SCD Types 1/2/3 • Advanced SQL proficiency — complex joins, window functions, CTEs, aggregations for financial calculations • Experience with cloud data platforms: Snowflake, AWS Redshift, Azure Synapse, Google BigQuery, or Databricks • Familiarity with ETL/ELT tools and pipeline orchestration: dbt, Apache Airflow, AWS Glue, Azure Data Factory, or similar • Understanding of data modelling tools: ERwin, dbdiagram.io, Lucidchart, or equivalent • Exposure to data catalogue and lineage tools: Collibra, Alation, Apache Atlas, or similar Preferred / Nice-to-Have • Experience with FP&A platforms: Anaplan, OneStream, Adaptive Insights, or TM1 • Exposure to regulatory reporting data architectures (BCBS 239, FINREP, COREP, or similar) • Familiarity with data mesh, data product thinking, or federated data governance models • Experience with BI/visualization tools: Power BI, Tableau, Looker — particularly for financial reporting use cases • Python or PySpark scripting ability for data profiling, exploration, or validation automation • Knowledge of Master Data Management (MDM) for financial hierarchies (legal entity, cost center, product) • Professional certification: CBAP, PMI-PBA, CFA (partial), or ACCA is a strong advantage • Experience in retail, banking, insurance, or multi-currency / multi-entity enterprise environments What Success Looks Like • Finance stakeholders trust you to own their data definitions — you are the single source of truth for what a metric means • Data engineers receive specs so precise that implementation ambiguity is near-zero • Financial reports powered by your data models reconcile to source systems within agreed tolerance thresholds • Your data dictionaries and documentation are treated as living assets, not one-time deliverables • You reduce time-to-insight for Finance teams by proactively identifying data quality issues before they surface in reports • You are known as the bridge — respected by Finance for your technical credibility, and by Engineering for your business fluency