Financial Data Analyst

Title: Financial Data Business Analyst / Functional Engineer 100% Remote Long Term Contract On CC2 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 engineering-ready 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: 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, chartof 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 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 financialanalytics workloads • Define entity-relationship diagrams, data flow diagrams, and source-to-target mappings • Enforce data modellingstandards, naming conventions, and governance policiesacross the data platform • Collaborate with Data Architects to ensure financialmodels align with enterprise data model and master data strategy Data Engineering Collaboration & Functional Oversight • Produce detailed functional specifications for data pipelines, ETL/ELTtransformations, and aggregation logic • Collaborate closely with data engineersto review and validate pipelineimplementations against business rules • Write and review SQL for data validation, business logic verification, and analytical queries • Define data qualityrules, reconciliation checks,and acceptance criteriafor financial data loads • Participate in data model reviews,sprint planning, and backlog groomingwithin an Agile delivery framework 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 Reporting & Analytics Enablement • Work with BI and Analytics teams to design semantic layers and reporting modelsfor 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/COmodules), 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 non-technical 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/ELTtools and pipelineorchestration: dbt, ApacheAirflow, 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, ApacheAtlas, or similar Preferred / Nice-to-Have • Experience with FP&Aplatforms: 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 PySparkscripting ability for data profiling, exploration, or validation automation • Knowledge of MasterData 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 theirdata definitions — you are the single source of truth for what a metric means • Data engineers receivespecs so precisethat implementation ambiguityis near-zero • Financial reports poweredby your data models reconcileto source systemswithin 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 qualityissues 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.