Director, Analytics Infrastructure – Pipeline Operations

Job not on LinkedIn

🔥 1 hour ago

🇺🇸 United States – Remote

💵 $194.6k - $361.4k / year

⏰ Full Time

🔴 Lead

⚙️ Operations

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Novartis

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1996

🏥 Healthcare

💼 Consulting

🏭 Manufacturing

Healthcare • Consulting • Manufacturing

Novartis is an innovative medicines company dedicated to reimagining medicine to improve and extend people’s lives. With a presence in over 250 million patients worldwide, Novartis focuses on harnessing scientific breakthroughs to develop transformative, high-value treatments in areas such as cardiovascular, renal and metabolic diseases, immunology, neuroscience, and oncology. The company is committed to diversity, equity, and inclusion, and aims to create a positive impact through societal contributions and environmental sustainability efforts. Furthermore, Novartis engages in research collaborations and embraces modern technology platforms to advance their research and development capabilities, making them a leader in the global healthcare industry.

📋 Description

• 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 • 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

🏖️ Benefits

• Health, life, and disability benefits • 401(k) with company contribution and match • Generous time off package including vacation, personal days, holidays, and other leaves • Performance-based cash incentive • Eligibility for annual equity awards