The Business Analyst (BA) plays a critical role in enabling successful Data Platform and Data Engineering outcomes by translating business objectives into clear, delivery‑ready requirements for data solutions. This role focuses on defining data needs, analytical intent, downstream use cases, and operational expectations, ensuring data products are reliable, scalable, and aligned to enterprise standards. The BA partners closely with Technical Delivery Managers (TDMs), Data Engineers, Solution Architects, and business stakeholders to drive effective execution across data pipelines, platforms, and analytics enablement initiatives
Key Responsibilities
Data Requirements & Definition
- Lead discovery with business and technical stakeholders to understand data use cases, decision drivers, and analytical outcomes.
- Translate business needs into data‑focused requirements, including source systems, grain, key dimensions, metrics, calculation logic, and data quality expectations.
- Produce clear, executable Jira tickets and user stories for data pipelines, transformations, models, views, and datasets.
- Define acceptance criteria covering data correctness, completeness, timeliness, and usability.
- Maintain durable documentation such as data definitions, process flows, assumptions, and decision logs.
Execution & Delivery Support
- Actively support Data Engineers during build and test phases by answering requirement and business‑logic questions in real time.
- Help resolve data ambiguity, edge cases, and scope gaps uncovered during implementation.
- Partner with TDMs to support sprint planning, backlog refinement, and delivery readiness for data workstreams.
- Participate in standups, planning sessions, and retrospectives as a core delivery team member.
Collaboration & Platform Alignment
- Partner with Solution Architects to ensure requirements align with platform standards, architectural patterns, and long‑term scalability goals.
- Coordinate across upstream source teams and downstream consumer teams (BI, analytics, ML, operations) to surface dependencies and impacts early.
- Help ensure data solutions preserve business intent while fitting within established Data Platform and governance frameworks.
Continuous Improvement & Enablement
- Improve consistency and quality of data tickets, documentation, and handoffs to reduce rework and clarifying cycles.
- Contribute to retrospectives and process reviews to strengthen data delivery patterns and BA practice maturity.
- Identify opportunities for automation, standardization, and reuse within data discovery and documentation processes.