Note: The job is a remote job and is open to candidates in USA. Novartis is a life sciences company focused on developing innovative treatments and improving patients’ lives. The Director, Analytics Infrastructure & Pipeline Operations will build AI-powered data pipelines, scalable repositories, feature stores, and self-service data capabilities that support enterprise data science and analytics. The role also leads platform optimization, data integration, and monitoring for reliable analytics infrastructure.
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
Skills
- 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
Benefits
- Your compensation will include a performance-based cash incentive
- 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.
- US-based eligible employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves
- Remote work arrangement
Company Overview