Director, Analytics Engineering (2 Openings)

Novartis is hiring a Director, Analytics Engineering (2 Openings) for a remote role open to applicants in United States. It pays $195k-$361k a year and Level rates it ; you can apply on Novartis.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting lists this role as remote.
Tools and skills the posting cites include SageMaker, Databricks and Feast.
Level classifies it as a lead role.
Level lists 870 other open roles at Novartis.

AI in this role

Director of Analytics Engineering at Novartis leading the creation of AI-powered data pipelines, feature stores, and scalable data platforms.

SageMakerDatabricksFeastTecton

ml-opsfeature-engineeringdata-pipelinesdata-engineeringpythonmlops

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, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.

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 to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or 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.

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

How we rate this

Director, Analytics Engineering (2 Openings) at Novartis rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Director, Analytics Engineering (2 Openings) is an open job at Novartis.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $195k-$361k a year.
The role is based in Remote Position (USA) and is marked remote.
The job was posted on September 14, 2026.
Seniority: lead.
Applications are handled by Novartis. Apply on Novartis

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsFeature EngineeringData PipelinesData EngineeringPythonMLOpsSageMakerDatabricks

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where feature engineering was part of your work. What did you do?
Tell me about a project where data pipelines was part of your work. What did you do?
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where python was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, Feature Engineering, Data Pipelines, Data Engineering, and Python. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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