The Data Business Analyst works at the intersection of business stakeholders, subject matter experts (SMEs), and delivery teams to define, refine, and document reporting and data warehouse requirements. The role translates non‑technical business needs into clear, testable technical specifications for data engineers, BI/reporting engineers, and QA teams—ensuring that reporting outputs are accurate, compliant, auditable, and aligned to business outcomes.
Key Responsibilities
Stakeholder Engagement & Discovery
- Facilitate workshops/interviews with SMEs (Finance, Credit Risk, Compliance, Operations, Collections, Product) to elicit reporting needs.
- Challenge and refine requirements by asking “why,” identifying underlying business problems, and proposing pragmatic options.
- Manage stakeholder expectations and ensure alignment on scope, timelines, and deliverables.
Requirements Definition (New Reports & Amendments)
- Capture and document: report objectives, users, frequency, distribution channels, and operational processes.
- Report layout/logic (KPIs, dimensions, measures, filters, drill‑down paths, grouping).
- Regulatory/compliance needs (e.g., audit trail, retention, data provenance).
- Define functional requirements and non‑functional requirements (performance, security, access, availability).
- Create clear acceptance criteria and ensure testability (Given/When/Then where suitable).
Translation: Business Language → Technical Specifications
- Translate business requirements into: data definitions (business glossary terms, calculations, thresholds); source‑to‑target mappings (STTM) with field‑level transformations; reporting data structures and modelling requirements (facts, dimensions, grain, historical treatment); data lineage and reconciliation rules.
- Produce deliverables that engineers can build from without ambiguity.
Data Warehouse & Reporting Enablement
- Partner with data engineers to define target warehouse structures and reporting semantic models.
- Align on grain, keys, conformed dimensions, and metric definitions.
- Validate data availability and suitability of sources.
- Support change impact assessments across upstream sources, ETL/ELT pipelines, and downstream reports.
- Define data quality rules (completeness, accuracy, timeliness, validity, uniqueness) and control checks.
- Specify reconciliations between source systems, warehouse tables, and report totals.
- Contribute to audit readiness: evidence, traceability, and control documentation.
Delivery Support (Agile / Hybrid)
- Own and maintain a prioritised backlog of reporting and data warehouse items.
- Write user stories, epics, and spikes; support sprint planning and refinement.
Testing & UAT Support
- Support the creation of UAT plans and scripts.
- Support QA and end users during UAT; triage defects, clarify requirements, and coordinate fixes.
- Validate outputs against business expectations and reconciliation rules.
Documentation & Governance
- Document JIRA tickets, report specifications and calculation logic, change requests and impact assessments.
- Ensure artefacts are stored in approved repositories (e.g., Confluence, SharePoint) and are audit‑friendly.
Technical Skills and Experience
- Requirements elicitation, stakeholder management, facilitation and workshop leadership.
- Ability to handle ambiguity and converge on clear, testable outcomes.
- Strong documentation, traceability, and change control discipline.
- Understanding of data warehousing concepts (facts/dimensions, grain, conformed dimensions).
- SQL fundamentals (ability to validate datasets and reconcile outputs).
- Reporting/BI concepts (semantic layers, measures, row‑level security).
- Awareness of data sensitivity, privacy, and access controls (least privilege).
- Aware of audit expectations and evidence‑based delivery.
- Regulatory reporting mindset and operational resilience principles (where applicable).
- Ability to translate between SMEs and technical teams without losing meaning.
- Clear writing style and strong visualisation of complex logic.
- Proficiency in using JIRA, Confluence, Power BI.
- Experience with Tableau, SSRS, Looker, or similar.
- Exposure to cloud data platforms (Azure Synapse, Databricks, Snowflake) and orchestration (ADF).
- Familiarity with data governance tooling (Collibra, Alation) and data cataloguing.
- Knowledge of testing approaches for data pipelines and reports.
- Performance delivery, teamwork, decision making, values and ethics.
Benefits
- Up to 25 days’ annual leave + Bank Holidays.
- Your birthday off, every year.
- A contributory pensions scheme, matched up to 5 %.
- Long Service Awards.
- Cycle to work scheme.
- Life Assurance.
Equal Opportunities
We are committed to encouraging equality, diversity and inclusion and aim to create a working environment where every employee is respected.
We will provide fairness, and respect to all our prospective employees, and all hiring decisions are based on merit.
We aim to ensure that no job applicant is placed at a disadvantage by practices or requirements that disproportionately disadvantage protected groups, and which are not justified by the demands of the role.
Everyone is welcome at Evolution Money! We are proud in creating an inclusive and diverse culture in our Evo Team community. We want to ensure that you feel comfortable and can give your best throughout the recruitment process. We encourage applications from all backgrounds and communities, and we are more than happy to discuss any reasonable adjustments that you may require. Please get in touch with us at any stage of recruitment to discuss it: evohr@evolutionmoney.co.uk
Evolution Money is a Disability Confident Committed employer. We offer interview to anyone with disability who meets the minimum criteria for the role.
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