Director Analytics Infrastructure, Pipeline Operations

Novartis has an exciting opportunity for a Director, Analytics Infrastructure & Pipeline Operations. This role is responsible for building next-generation, AI-powered automated data pipelines and scalable data repositories that enable enterprise data science and analytics at scale. By leveraging advanced AI technologies, modern data engineering tools, and feature engineering platforms, this director creates self-service, analytics-ready datasets and enterprise feature stores that empower both expert data scientists and citizen data scientists to rapidly develop, deploy, and scale models.

This position can be based remotely anywhere in the U.S. (there may be some restrictions based on legal entity). Please note that this role would not provide relocation as a result. The expectation of working hours and travel (domestic and/or international) will be defined by the hiring manager.

This position will require 20% travel.

• 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.

• 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.

• 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.

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

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

Skills Desired Agile (Programming Methodology) (Inactive), Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Strategies, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Organization Awareness (Inactive), Stakeholder Engagement, Statistical Analysis, Time Series Analysis, Vision (Inactive)