The Lead Product AI Data Engineer will define, build, and scale enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare ecosystem, combining data engineering and dimensional modeling with enterprise architecture leadership.
Responsibilities
- Define and evolve enterprise-level product data architecture across multiple product lines, ensuring scalability, reliability, and AI/ML readiness.
- Architect scalable ETL/ELT pipelines and distributed data workflows for analytics, AI, and product intelligence.
- Develop and enforce dimensional data modeling standards, including star schemas and snowflake schemas.
- Design and maintain fact and dimension tables, covering proper grain, SCD handling, hierarchical dimensions, and high-performance query patterns.
- Establish data architecture principles, including naming conventions and best practices for ETL/ELT, event tracking, and AI pipelines.
- Act as the technical authority to guide architecture decisions that meet product, platform, and AI requirements.
- Partner with cross-functional teams to translate requirements into scalable, analytics- and AI-ready data models and pipelines.
- Curate and validate datasets for machine learning, experimentation, and advanced analytics.
- Evolve event-driven architectures to align with dimensional modeling and downstream analytics.
- Establish feature store frameworks and reusable AI data pipelines across multiple products.
Requirements
- 10+ years of professional experience in Data Engineering, Analytics Engineering, or Data Architecture.
- Proven experience in enterprise-scale data architecture and distributed pipeline design.
- Expert-level proficiency in SQL and relational database design.
- Strong hands-on experience in Python for pipeline automation, orchestration, and data framework development.
- Deep expertise in dimensional modeling, including star and snowflake schemas, fact/dimension tables, SCDs, surrogate keys, and hierarchical dimensions.
- Experience designing and operating production-grade ETL/ELT pipelines for analytics and AI/ML workloads, with the ability to influence technical outcomes through architectural leadership and enterprise strategy.
- Experience with cloud data warehouses: Snowflake, Databricks, BigQuery.
- Familiarity with modern orchestration and transformation tools: dbt, Airflow, Fivetran, Segment.
- Experience handling semi-structured and event-driven data, including JSON, logs, and clickstream.
- Exposure to BI and visualization tools: Power BI, Tableau, Looker, SAP BusinessObjects.
- Experience with AWS, Azure, or GCP, including data governance, security, and compliance frameworks.
- Background in Life Sciences or Healthcare analytics.
Technologies
SQL, Python, Snowflake, Databricks, BigQuery, dbt, Airflow, Fivetran, Segment, JSON, Power BI, Tableau, Looker, SAP BusinessObjects, AWS, Azure, GCP, ETL/ELT, Machine learning, AI/ML, SCDs, star schemas, snowflake schemas, fact/dimension tables, surrogate keys, hierarchical dimensions.
AI and Product Data Enablement
- Partner with cross-functional teams to translate requirements into highly scalable, analytics- and AI-ready data models and pipelines.
- Curate and validate datasets for machine learning, experimentation, and advanced analytics.
- Evolve event-driven architectures to align with dimensional modeling and downstream analytics.
- Establish feature store frameworks and reusable AI data pipelines across multiple products.
Location
Ann Arbor, MI (onsite)
