Job description

Band Level 6 Job Description Summary #LI-Remote Novartis has an exciting opportunity for a Director, Analytics Engineering. 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. There are 2 positions available. Job Description Major accountabilities: 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; 7 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. To learn more about the culture, rewards and benefits we offer our people click here . EEO Statement: The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential. Accessibility and reasonable accommodations The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms novartis.com call 1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message. novartis.com Salary Range $194,600.00 - $361,400.00 Skills Desired Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Stakeholder Engagement, Statistical Analysis, Time Series Analysis

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Job Description Summary
There are 2 positions available.
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;
7 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.

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aimlpythonsqlci/cdaccessibilitymachine learning

Platform: workday
Effort: long

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401(K)