Data & Privacy Risk Specialist Manager

Role Overview

• 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: Data Privacy Impact Assessment pipeline performance and thematic assessment
• Retention and deletion controls
• Records classification and maintenance
• Metadata, lineage and lifecycle verification
• Develop oversight routines focused on clarity, repeatability and high‑quality evidence.
• Partner with engineering, platform and architecture teams to ensure analytical methods and evidence genuinely enable effective challenge.
• Provide structured challenge and insight on data privacy, retention and records‑related risks.
• Produce high‑quality oversight outputs, including reports, summaries and committee materials.
• Support incidents and issues using a data‑driven diagnostic mindset.
• Apply developing regulatory and risk knowledge to contextualise findings.
• Communicate analytical findings in clear, accessible language.
• Act as the lead for the team, improving analytical maturity and embedding a precision‑led oversight culture.

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.
• Familiarity with data protection principles (e.g., personal data, minimisation, retention).
• Exposure to DPIAs, privacy‑by‑design or related governance practices.

Tech Stack

• Python
• SQL
• Tableau

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