Analyse large volumes of production data to investigate issues, anomalies, or questions raised by the team or data consumers.
Identify and assess issues such as missing, duplicated, invalid, or incomplete data, including understanding scale, impact, and risk.
Distinguish between historic data issues and ongoing problems.
Use PySpark and SQL to explore data and validate assumptions.
Present findings clearly and proportionately to support decision-making.
Act as a point of support for data scientists and data engineers using the platform.
Investigate concerns raised through queries or analysis conducted by others.
Help users understand unexpected results, data behaviour, or platform constraints.
Clarify whether issues represent defects, unsupported use cases, or gaps in understanding.
Gather requirements from data consumers through one-to-one conversations and workshops.
Facilitate discussions to understand different perspectives and needs across consumer groups.
Translate outcomes into epics and tickets.
Recommend priorities and sequencing, applying techniques such as MoSCoW.
Balance consumer needs with data security and access controls.
Interpret and apply complex data governance documentation.
Identify platform changes that may require governance updates.
Own or contribute to updates to governance artefacts.
Support other teams in understanding governance boundaries and responsibilities.
Identify and raise gaps or conflicts in governance arrangements.

Strong analytical skills, with confidence working directly with production data.
Experience using SQL and data analysis tools to investigate complex data questions.
Ability to translate technical findings into clear, decision-focused narratives.
Strong stakeholder engagement and workshop facilitation skills.
Experience working in highly governed or regulated data environments.
Ability to apply judgement about evidence, risk, and proportionate analysis.

Data Operations Business Analyst

Key skills