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Strong brand, metro location, and mid-level generalist Data Engineer role increase candidate competition.
Requires financial services and actuarial domain familiarity, but core data engineering skills remain moderately transferable.
Explicit 4-10 years, domain-specific data engineering skills, Azure/Databricks/Spark and audited FS experience tighten filters.
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Operate, maintain, and update data pipelines that validate, transform, and enrich policyholder data for actuarial modelling and assumption setting.
Ensure actuarial input data completeness, accuracy, reconciliation, and availability with implemented checks and controls.
Investigate and resolve data issues proactively and communicate findings clearly to stakeholders to support confident decision-making.
4-10 years of relevant Data Engineering experience.
Bachelor’s or Master’s degree.
Strong skills in SQL, data management, modelling, and experience maintaining documentation and audit evidence.
Experience working in Financial Services or similar regulated environment with focus on data governance and auditability.
Experienced in building and maintaining scalable, controlled data pipelines in financial or actuarial contexts.
Able to independently manage workload and communicate complex data issues to both technical and non-technical stakeholders.
Skilled in using Excel (advanced), Power BI, and Python/PySpark for data analysis and automation in a heavily audited environment.