Director, Analytics Engineering (2 Openings)

What you'll do

• Design intelligent, self-healing data pipelines and automated feature engineering workflows with data-quality monitoring and lineage tracking.
• Build centralized feature stores, curated data repositories, and self-service data access layers for analytics and machine-learning use cases.
• Partner with Enterprise IT to optimize analytics platforms and establish data availability, freshness, quality, and observability standards.

What they ask for

• Advanced degree in Computer Science, Data Engineering, or a 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.
• Expertise in feature store technologies and modern data platforms optimized for ML workloads.
• Proficiency in Python, SQL, and Spark/PySpark; experience with data orchestration, CI/CD, and data governance, privacy, and compliance.

Nice to have

• Experience in pharmaceutical, healthcare, or life sciences.
• Knowledge of streaming technologies, MLOps tools, and data lakehouse architecture.

Pay

$194,600.00 - $361,400.00 USD base salary

Tools

Feast, Tecton, SageMaker Feature Store, Databricks Feature Store, Databricks, Auto ML, Snowflake, BigQuery

AI focus

Builds AI-powered automated data pipelines, including ML-based data-quality monitoring, anomaly detection, and remediation; creates feature stores and feature engineering workflows for model development and deployment.