Financial Data Business Analyst / Functional Engineer

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Central Business Solutions, is seeking the following. Apply via Dice today!Financial Data Business Analyst / FunctionalEngineerRole DetailsLocation: HybridExperience: 5–9 years (Business Analysis + Finance Data + Data Engineering)Role OverviewWe are looking for a rare hybrid professional who bridges the gap between Finance domain expertise andmodern data engineering practice. As a Financial Data Business Analyst / Functional Engineer, you willserve as the connective tissue between Finance stakeholders and data platform teams — translatingcomplex 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 andbusiness rules, to designing dimensional models and defining transformation logic, to validating that whatgets built matches what the business actually needs. You are equally comfortable in a CFO’s strategysession 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, andvalidate data requirements Translate business concepts — P&L structures, chart of accounts, cost center hierarchies, budgetvs. 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 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 dataplatform Collaborate with Data Architects to ensure financial models align with enterprise data model andmaster data strategy Data Engineering Collaboration & Functional Oversight Produce detailed functional specifications for data pipelines, ETL/ELT transformations, andaggregation logic Collaborate closely with data engineers to review and validate pipeline implementations againstbusiness 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 deliveryframework 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 models for financialdashboards 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) formanagement reporting Support self-service analytics by producing clear data model documentation consumable bybusiness usersRequired QualificationsFinance 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, BalanceSheet, 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, orequivalent Working knowledge of accounting standards relevant to data: IFRS 15/16, US GAAP revenuerecognition, or SOX controlsBusiness Analysis Proven ability to produce high-quality FRDs, BRDs, data dictionaries, and source-to-targetmapping 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 squadsData Engineering & Modelling Solid understanding of data warehousing concepts: Kimball/Inmon methodology, dimensionalmodelling, SCD Types 1/2/3 Advanced SQL proficiency — complex joins, window functions, CTEs, aggregations for financialcalculations Experience with cloud data platforms: Snowflake, AWS Redshift, Azure Synapse, GoogleBigQuery, or Databricks Familiarity with ETL/ELT tools and pipeline orchestration: dbt, Apache Airflow, AWS Glue, AzureData 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 similarPreferred / 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 financialreporting 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 environmentsWhat Success Looks Like Finance stakeholders trust you to own their data definitions — you are the single source of truth forwhat 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 agreedtolerance 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 beforethey surface in reports You are known as the bridge — respected by Finance for your technical credibility, and byEngineering for your business fluency.