Director, Analytics Engineering (2 Openings)

Novartis is offering an opportunity for a Director of Analytics Engineering to build next-generation, AI-powered data infrastructure for enterprise data science and analytics. The role leads the development of intelligent data pipelines, feature stores, curated data repositories, and self-service data access layers that support scalable model development and deployment.

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 datasets, evaluation datasets, and production serving layers
• 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
• 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 ability to create scalable, self-service capabilities that reduce dependency on data engineering
• Experience in pharmaceutical, healthcare, or life sciences industry
• Knowledge of streaming technologies, MLOps tools, and data lakehouse architecture