Lead Product AI Data Engineer

DR Decisions Resources LLC; Ann Arbor (MI); On-site; USD 150,000 - 190,000; Full time

Job summary

DR Decisions Resources LLC in Ann Arbor, MI seeks a Lead Product AI Data Engineer to define, build, and scale enterprise-grade AI-ready data platforms for Life Sciences and Healthcare. The role combines data engineering with dimensional modeling and enterprise architecture leadership.

You will lead cross-functional teams to architect scalable ETL/ELT pipelines, implement star and snowflake schemas, and guide data governance and security in cloud environments. This is an onsite role in Ann Arbor.

Qualifications

• 10+ years of experience in Data Engineering, Analytics Engineering, or Data Architecture.
• Expert in enterprise-scale data architecture and distributed pipelines.

Responsibilities

• Define and evolve enterprise-level product data architecture across multiple product lines.
• Architect scalable ETL/ELT pipelines and distributed data workflows for analytics and AI.
• Develop and enforce dimensional data modeling standards, including star and snowflake schemas.
• Design and maintain fact and dimension tables with proper grain, SCD handling, and high-performance queries.
• Establish data architecture principles, naming conventions, and best practices for ETL/ELT, event tracking, and AI pipelines.
• Act as technical authority to guide architecture decisions meeting product 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.

Job description

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)