Director, Analytics Engineering (2 Openings)

Stack

Responsibilities

• ▹Design and implement intelligent, self-healing data pipelines that leverage AI/ML for automated data quality monitoring, anomaly detection, and remediation
• ▹Build and maintain centralized feature stores that enable feature reusability across multiple models and use cases
• ▹Create curated data repositories optimized for data science/AI workflows, including training, evaluation, and production serving datasets
• ▹Develop automated feature engineering pipelines that transform raw data into analytics-ready features with lineage tracking
• ▹Partner with Enterprise IT to optimize analytics platform architecture for high-performance data science workloads
• ▹Build automated pipelines that integrate diverse data sources including sales, CRM, patient claims, real-world evidence, and unstructured data
• ▹Create self-service data access layers that empower data scientists and analysts to query and extract data independently
• ▹Establish SLAs for data availability, freshness, and quality; implement monitoring and observability solutions

Requirements

• ▹Advanced degree in Computer Science, Data Engineering, or related field
• ▹7+ years of experience in data engineering, ML/AI engineering, or analytics infrastructure
• ▹5+ years leading teams building enterprise-scale data platforms and feature stores
• ▹Expert knowledge of feature store technologies (Feast, Tecton, SageMaker Feature Store, Databricks Feature Store)
• ▹Deep expertise in modern data platforms optimized for ML workloads (Databricks, Auto ML, Snowflake, BigQuery)
• ▹Strong proficiency in Python, SQL, Spark/PySpark for large-scale data processing
• ▹Experience with data orchestration tools (Airflow, Prefect, dbt) and CI/CD for data pipelines
• ▹Understanding of data governance, privacy (HIPAA, GDPR), and compliance in life sciences

Nice to have

• ▹Proven track record of implementing AI/ML-powered automation in data engineering workflows
• ▹Strategic thinker who can balance innovation (cutting-edge AI tools) with reliability (production stability)
• ▹Builder mindset with the ability to create scalable, self-service capabilities that reduce dependency on the data engineering team

Soft skills