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Popular mid-level data role with common stack; insurance domain reduces applicant pool.
Core data engineering skills are transferable, though insurance domain experience increases specificity.
Specific Snowflake/PySpark and insurance domain experience required, but no explicit years.
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Design, develop, and maintain scalable ETL/ELT data pipelines supporting P&C insurance pricing, rating, underwriting, and actuarial analytics.
Collaborate with Pricing Actuaries, Data Scientists, Business Analysts, and Cloud Engineering to deliver data assets for predictive models, GLM pricing frameworks, rating updates, analytics, and regulatory reporting.
Build and optimize Snowflake-based data solutions and implement data quality frameworks supporting large-scale data processing and enterprise data modernization.
Strong expertise in SQL, Snowflake, PySpark, and Python.
Experience with insurance domain datasets: policy, claims, quote, coverage, and pricing is mandatory.
Bachelor's or Master's degree in CS, Engineering, Statistics, Mathematics, Data Science, or related field.
Work Experience Required: Not explicitly mentioned in the JD. Relevant cloud certifications (AWS, Snowflake, Databricks) preferred but not mandatory.
Experienced in building and optimizing cloud-based ETL/ELT pipelines at scale in insurance or actuarial analytics domains.
Comfortable working cross-functionally with actuaries, data scientists, and cloud engineering teams to translate business needs into data solutions.
Skilled in creating reusable, parameterized data processing frameworks and implementing batch and incremental ingestion pipelines in modern cloud environments.