Director, Analytics Infrastructure – Pipeline Operations

Novartis

Director of Analytics Infrastructure at Novartis responsible for building AI-powered data pipelines. Leading the development of data repositories and features for analytics at scale.

Posted 7/28/2026full-timeRemote • 🇺🇸 United StatesLead💰 $194,600 - $361,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing intelligent data pipelines leveraging AI/ML for automated data quality and anomaly detection. Proficient in building enterprise-scale data platforms and feature stores, with a strong focus on data governance and compliance in life sciences.

Highest-signal resume keywords

Data EngineeringMachine Learning EngineeringFeature Store TechnologiesData Orchestration ToolsData Governance

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Hard Skills

PythonSQLSparkPySparkFeature Store TechnologiesData ProcessingAutomated Feature EngineeringData Quality MonitoringAnomaly DetectionData Integration

Tools & Technologies

DatabricksAirflowPrefectDbtAuto MLSnowflakeBigQuerySageMaker Feature StoreFeastTecton

Industry Keywords

Data GovernanceHIPAAGDPRLife SciencesAnalytics InfrastructureData AvailabilityData FreshnessData QualitySelf-Service Data AccessData Science Workflows

Tech Stack

Tools & technologies

AirflowBigQueryPySparkPythonSparkSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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