Director, Analytics Engineering (2 Openings)

About the role

Novartis is looking for a Director, Analytics Engineering to build next-generation, AI-powered automated data pipelines and scalable data repositories that support enterprise data science and analytics at scale. The role creates self-service, analytics-ready datasets and enterprise feature stores so that both expert and citizen data scientists can quickly develop, deploy and scale models. There are two openings, and the position can be based remotely anywhere in the U.S., though there may be restrictions depending on the legal entity.

Key responsibilities

• Design and implement intelligent, self-healing data pipelines using AI/ML for automated data quality monitoring, anomaly detection and remediation.
• Build and maintain centralized feature stores that enable feature reuse across models and use cases.
• Create curated data repositories optimized for data science and AI workflows, including training, evaluation and production serving layers.
• Develop automated feature engineering pipelines with lineage tracking.
• Partner with Enterprise IT to optimize the analytics platform for high-performance data science workloads.
• Integrate diverse data sources such as sales, CRM, patient claims, real-world evidence and unstructured data.
• Create self-service data access layers for data scientists and analysts.
• Establish SLAs for data availability, freshness and quality, and implement monitoring and observability.

Required profile

• 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 that built enterprise-scale data platforms and feature stores.
• Understanding of data governance, privacy (HIPAA, GDPR) and compliance in life sciences.
• Preferred: experience in pharmaceutical, healthcare or life sciences, and a track record of AI/ML-powered automation in data engineering.

Required skills

• Feature store technologies (Feast, Tecton, SageMaker Feature Store, Databricks Feature Store)
• Data platforms (Databricks, Auto ML, Snowflake, BigQuery)
• Python, SQL, Spark/PySpark
• Data orchestration (Airflow, Prefect, dbt) and CI/CD for data pipelines
• Preferred: streaming technologies, MLOps tools and data lakehouse architecture

What we offer

• Expected salary range of $194,600 to $361,400 per year, with a performance-based cash incentive and possible annual equity awards.
• Benefits including health, life and disability coverage, a 401(k) with company contribution and match, and a time-off package.
• 20% travel expected; no relocation provided.