Principal Product AI Data Platform Engineer

• 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.
• Strong ability to influence technical outcomes through architectural leadership and enterprise strategy.

It would be great if you also

• Experience with cloud data warehouses: Snowflake, Databricks, BigQuery.
• Familiarity with modern data orchestration and transformation tools: dbt, Airflow, Fivetran, Segment.
• Experience handling semi-structured and event-driven data (JSON, logs, 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 will be a big plus

What will you be doing in this role

• 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 (star schemas, snowflake schemas) across the organization.
• Design and maintain fact and dimension tables, ensuring proper grain, SCD handling, hierarchical dimensions, and high-performance queries.
• Establish data architecture principles, naming conventions, and best practices for ETL/ELT, event tracking, and AI pipelines.
• Serve as the technical authority guiding architecture decisions to meet product, platform, and AI requirements.

AI & 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

• 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.
• Strong ability to influence technical outcomes through architectural leadership and enterprise strategy.

It would be great if you also

• Experience with cloud data warehouses: Snowflake, Databricks, BigQuery.
• Familiarity with modern data orchestration and transformation tools: dbt, Airflow, Fivetran, Segment.
• Experience handling semi-structured and event-driven data (JSON, logs, 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 will be a big plus

What will you be doing in this role

• 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 (star schemas, snowflake schemas) across the organization.
• De