Principal Product AI Data Platform Engineer

As a Principal Product AI Data Engineer / Architect at our company, you will play a crucial role in defining, building, and scaling enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare ecosystem. Your responsibilities will combine technical expertise with architecture leadership to influence multiple product lines and set long-term standards for dimensional modeling, analytics pipelines, and AI data enablement. Key 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 (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. - 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. Qualifications Required: - 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. - 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. Please note that at our company, Clarivate, we are committed to providing equal employment opportunities for all qualified persons with respect to hiring, compensation, promotion, training, and other terms, conditions, and privileges of employment. We comply with applicable laws and regulations governing non-discrimination in all locations. As a Principal Product AI Data Engineer / Architect at our company, you will play a crucial role in defining, building, and scaling enterprise-grade AI-ready data platforms for the Life Sciences and Healthcare ecosystem. Your responsibilities will combine technical expertise with architecture leadership to influence multiple product lines and set long-term standards for dimensional modeling, analytics pipelines, and AI data enablement. Key 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 (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. - 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. Qualifications Required: - 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