Director, Analytics Engineering (2 Openings)
Core
Design and implement AI-powered automated data pipelines, scalable data repositories, and enterprise feature stores to enable data science and analytics at scale.
Role type
Director, Analytics Engineering
Builds
Self-service analytics-ready datasets, enterprise feature stores, and automated data pipelines for data science workflows.
Domain
Life Sciences / Data Engineering / AI Infrastructure
Deliverable
production ML models | infrastructure
Required skills
Feature store technologies (Feast, Tecton, SageMaker Feature Store, Databricks Feature Store), Modern data platforms (Databricks, Snowflake, BigQuery), Python, SQL, Spark/PySpark, Data orchestration (Airflow, Prefect, dbt), CI/CD for data pipelines, Data governance and compliance (HIPAA, GDPR)
Preferred skills
AI/ML-powered automation in data engineering, Streaming technologies, MLOps tools, Data lakehouse architecture, Pharmaceutical/healthcare industry experience
Responsibilities
Design self-healing data pipelines with AI/ML for quality monitoring and anomaly detection; Build centralized feature stores for feature reusability; Create curated data repositories for training, evaluation, and production serving; Develop automated feature engineering pipelines with lineage tracking; Partner with Enterprise IT to optimize analytics platform architecture; Integrate diverse data sources including sales, CRM, patient claims, and unstructured data; Establish SLAs for data availability, freshness, and quality with monitoring solutions
Seniority
Director, strategic leadership with hands-on technical execution
