The Lead Product AI Data Engineer / Architect will define, build, and scale enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare ecosystem. This role combines deep dimensional modeling and data engineering with enterprise architecture leadership to enable analytics and AI across multiple product lines.
Role 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, including proper grain, SCD handling, hierarchical dimensions, and high-performance query patterns.
- Establish data architecture principles, naming conventions, and best practices for ETL/ELT, event tracking, and AI pipelines.
- Act as the technical authority guiding architecture decisions to 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.
Required Experience and Qualifications
- 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, along with strong architectural leadership that influences technical outcomes.
- Experience with cloud data warehouses: Snowflake, Databricks, BigQuery.
- Familiarity with data orchestration and transformation tools: dbt, Airflow, Fivetran, Segment.
- Experience handling semi-structured and event-driven data, including JSON, logs, and clickstream data.
- 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.
Location
Ann Arbor, MI (onsite)
Key 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.
