Director, Analytics Engineering

About the role

Lead the creation of AI-driven, automated data pipelines and large data repositories so that data science and analytics can operate at enterprise scale. The role focuses on self-service, analytics-ready datasets and feature stores.

What you will do

• Design smart pipelines that use AI and machine learning to check data quality, spot anomalies and fix issues automatically
• Maintain central feature stores so features can be reused across many models and use cases
• Build curated repositories for AI work, covering training data, evaluation data and serving layers
• Create automated feature engineering flows that turn raw data into ready-to-use features with lineage tracking
• Work with enterprise IT to tune analytics platform architecture for demanding data science workloads
• Set service levels for data availability, freshness and quality, with monitoring and observability in place

What they look for

• Advanced degree in computer science, data engineering or a similar discipline
• Seven or more years in data engineering, machine learning engineering or analytics infrastructure
• Five or more years leading teams that build enterprise-scale data platforms and feature stores
• Expert knowledge of feature store tools such as Feast, Tecton, SageMaker Feature Store or Databricks Feature Store
• Hands-on strength in Python, SQL and Spark or PySpark for large-scale processing
• Familiarity with orchestration tools such as Airflow, Prefect or dbt, and CI/CD for data pipelines

Nice to have

• Record of bringing AI and machine learning automation into data engineering workflows
• Strategic thinker who balances new AI tools with production stability
• Builder mindset focused on scalable self-service that reduces reliance on data engineering
• Background in pharmaceutical, healthcare or life sciences work, plus knowledge of streaming, MLOps and lakehouse architecture