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Tier-1 brand, metro location, generalist Data Engineer title, and mid-level experience range increases candidate competition.
Actuarial and audited financial services context demands domain knowledge, limiting cross-industry transferability.
Explicit 4-10 years requirement, financial services audit controls, and core SQL/Python skills enforce moderate screening.
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Operate, maintain, and update data pipelines that validate, transform, and enrich policyholder data for actuarial modelling and assumption setting.
Ensure data accuracy, completeness, and consistency through controls and process improvements while managing Model Points Data Production and Experience Analysis tasks.
Collaborate with stakeholders, investigate and resolve data issues, and maintain all documentation, runbooks, and governance standards relevant to data processes.
4-10 years of relevant Data Engineering experience, preferably in Financial Services or a heavily audited environment.
Bachelor’s or Master’s Degree; Data Engineering certifications are advantageous but not mandatory.
Strong skills in SQL, data management and modelling, and experience with Excel (advanced formulas, pivot tables, Power Query) and optionally Power BI or Python for data analysis and automation.
Experience maintaining documentation, controls, and governance evidence for auditability and operational resilience; familiarity with data governance best practices.
Experienced data engineer comfortable working independently within established frameworks in Financial Services or similar regulated environments.
Skilled in building and maintaining scalable, reliable data pipelines focused on accuracy and compliance for actuarial use cases.
Capable of clear communication with technical and non-technical stakeholders and proactive in identifying and resolving data issues and improving processes.