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Mid-level data-engineer role with common skillset but niche AI/product focus reduces applicant density.
Requires specialized data architecture skills with preferred Life Sciences experience, moderately limiting cross-industry fit.
Explicit 5+ years plus mandatory enterprise data architecture, tooling, and domain skills create strict filters.
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Define and evolve scalable, reliable enterprise-level data architecture and dimensional modeling standards for multiple product lines in Life Sciences and Healthcare.
Architect, develop, and maintain production-grade ETL/ELT pipelines and data workflows optimized for analytics, AI, and product intelligence.
Lead cross-functional teams by setting technical standards and establishing reusable AI data pipelines and feature store frameworks across products.
5+ years of professional experience in Data Engineering, Analytics Engineering, or Data Architecture.
Expert-level proficiency in SQL, relational database design, and Python for pipeline automation and data framework development.
Experience with dimensional modeling (star and snowflake schemas, fact/dimension tables, SCDs) and enterprise-scale data architecture.
Familiarity with cloud data warehouses (Snowflake, Databricks, BigQuery) and modern data orchestration tools (dbt, Airflow).
Strong background in Life Sciences or Healthcare analytics environments to align with domain-specific data needs.
Proven ability to provide architectural leadership and influence enterprise-wide data engineering standards and strategies.
Experience working in hybrid environments with cross-functional teams to translate complex requirements into scalable AI-ready data architectures.