Director Analytics Infrastructure, Pipeline Operations

Key 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.

Essential Requirements

• Advanced degree in Computer Science, Data Engineering, or related field;
• 10+ 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.

Preferred Qualities

• 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.

Novartis Compensation Summary:

The salary for this position is expected to range between $194,600 and $361,400 per year.

The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves