Data Privacy Risk Manager leading the development of analytics-driven oversight in data privacy and risk management. Collaborating across teams to ensure compliance and effective data management practices.
Responsibilities
Lead development of a precision‑driven, analytics‑based oversight model for data privacy, retention and records risks.
Translate oversight questions into measurable indicators, diagnostics, and metrics.
Identify process weaknesses, control gaps and emerging risks using robust analytical methods.
Build reproducible reports, dashboards and analytical tools that deliver consistent, evidence‑based oversight.
Apply deep understanding of data lifecycles to assess how controls operate in real systems and processes.
Translate regulatory and policy requirements into measurable, testable, evidence‑backed controls.
Evaluate control maturity and effectiveness through precise, data‑accurate techniques.
Conduct oversight across various areas including Data Privacy Impact Assessments and records classification and maintenance.
Partner with engineering, platform and architecture teams to ensure analytical methods and evidence genuinely enable effective challenge.
Produce high‑quality oversight outputs, including reports and summaries.
Requirements
Hands‑on understanding of data lifecycles: collection, use, ingestion, transformation, storage, access, retention and deletion.
Ability to read, interpret and challenge code (e.g., SQL, Python) and assess analytical logic.
Experience with data modelling, pipelines, extract, transform, load concepts and working with engineering teams.
Proficiency with analytical tools (SQL, Python, Power BI, Tableau).
Ability to build automated or reproducible analytical outputs.
Understanding of metadata, data architecture concepts or AI/ML fundamentals.
Understanding of risk management, compliance or operational risk.
Interest in developing strong knowledge of data privacy, the Data Use Act, emerging digital and data laws, and the wider regulatory frameworks that impact how data is handled.
Demonstrated ability to learn complex theoretical frameworks quickly and apply them pragmatically.
Familiarity with data protection principles (e.g., personal data, minimisation, retention).
Benefits
A generous pension contribution of up to 15%
An annual performance-related bonus
Share schemes including free shares
Benefits you can adapt to your lifestyle, such as discounted shopping
30 days’ holiday, with bank holidays on top
A range of wellbeing initiatives and generous parental leave policies
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