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Mid-level popular data engineering role in metro locations with broad skillset requirements increases applicant competition.
Core data engineering skills transfer across industries but Life Sciences/Healthcare domain preference raises fit sensitivity moderately.
Explicit 5+ years and mandated enterprise data architecture, dimensional modeling, and cloud tool expertise makes shortlisting strict.
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Define and evolve scalable, enterprise-level AI-ready data architecture and dimensional modeling standards across multiple product lines.
Architect and maintain production-grade ETL/ELT pipelines and distributed data workflows for analytics, AI, and product intelligence.
Lead technical decisions on data architecture, serving as a technical authority for AI data enablement and cross-functional collaboration on data models and feature stores.
5+ years of professional experience in Data Engineering, Analytics Engineering, or Data Architecture.
Expert-level proficiency in SQL, relational database design, and strong hands-on Python experience for pipeline automation and orchestration.
Experience with cloud data warehouses (Snowflake, Databricks, BigQuery) and data orchestration tools (dbt, Airflow, Fivetran).
Familiarity with dimensional modeling concepts (star and snowflake schemas, fact/dimension tables, SCDs) and production-grade ETL/ELT pipeline design for AI/ML workloads.
Experienced in enterprise-scale data architecture leadership within Life Sciences or Healthcare analytics domains.
Skilled in translating cross-functional requirements into scalable, analytics- and AI-ready data platforms and pipelines.
Proficient in establishing data architecture principles, naming conventions, and reusable AI data pipeline frameworks across multiple products.