Director, Analytics Engineering (2 Openings)

Role: Director of Analytics Engineering responsible for designing and implementing AI-powered, automated data pipelines and scalable data repositories to support enterprise data science and analytics. Responsibilities include developing intelligent, self-healing pipelines with AI/ML for data quality, building centralized feature stores, creating curated data repositories for training, evaluation, and production, and enabling self-service data access for data scientists and analysts. The role involves partnering with IT to optimize platform architecture, integrating diverse data sources, and establishing SLAs for data quality and availability. Requirements: 7+ years in data engineering or ML/AI infrastructure; 5+ years leading enterprise-scale data platform teams; expertise in feature store technologies (Feast, Tecton, SageMaker, Databricks); strong skills in Python, SQL, Spark/PySpark; experience with orchestration tools (Airflow, Prefect, dbt); understanding of data governance and compliance in life sciences. Preferred: track record of AI/ML-powered automation, experience in pharma or healthcare, knowledge of streaming, MLOps, data lakehouse. The position can be remote across the U.S. with 20% travel, reporting to senior management, and involves cross-functional collaboration with IT, data science, and compliance teams. High-value specifics include focus on AI-driven data pipelines, feature stores, enterprise-scale data platforms, and life sciences regulatory considerations.