Director, Analytics Engineering

Novartis

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$194,600–$361,400 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $246k
This role $278k
$163k most similar roles pay here $383k

This role pays more than 73% of similar roles. Most pay $205,000–$286,700 — the shaded band above. At the midpoint, this role pays about $278k versus about $246k for comparable roles.

Based on 240 similar postings.

Employer

About Novartis

Novartis is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs in areas including oncology, immunology, neuroscience, and cardiology. Industry: Biopharmaceuticals

Novartis currently has 26 open roles on FindRole.

Listed pay typically runs $194,600–$361,400 across 26 roles with salary data.

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View all roles at Novartis

At a glance

TL;DR · Director, Analytics Engineering

The Director, Analytics Engineering leads the development of next-generation, AI-powered automated data pipelines and scalable repositories to enable enterprise data science. This role involves building intelligent, self-healing pipelines for automated quality monitoring, creating centralized feature stores for model reusability, and developing automated feature engineering workflows with lineage tracking. The position focuses on integrating diverse sources like sales, CRM, patient claims, and real-world evidence into curated datasets for training and production serving. Key technical requirements include expertise in Python, SQL, Spark/PySpark, and orchestration tools like Airflow, Prefect, or dbt. Candidates must be proficient with platforms such as Databricks, Snowflake, or BigQuery, and feature store technologies like Feast or Tecton. The work addresses complex data governance and compliance challenges within the pharmaceutical and life sciences sectors to provide self-service access for both expert and citizen data scientists.

What you'll do

  • Design and implement AI-powered, self-healing data pipelines for automated quality monitoring and anomaly detection.
  • Build and maintain centralized feature stores to enable feature reusability across multiple models and use cases.
  • Create curated data repositories optimized for training, evaluation, and production serving in AI workflows.
  • Develop automated feature engineering pipelines that transform raw data into analytics-ready features with lineage tracking.
  • Integrate diverse data sources including sales, CRM, patient claims, and unstructured data into unified pipelines.
  • Create self-service data access layers to allow data scientists and analysts to query data independently.
  • Establish and monitor SLAs for data availability, freshness, and quality using observability solutions.
  • Partner with Enterprise IT to optimize platform architecture for high-performance data science workloads.

What we're looking for

  • Advanced degree in Computer Science, Data Engineering, or a 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 such as Feast, Tecton, SageMaker Feature Store, or Databricks Feature Store.
  • Deep expertise in modern data platforms optimized for ML workloads like Databricks, Auto ML, Snowflake, or BigQuery.
  • Strong proficiency in Python, SQL, and 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 (preferred).
  • Experience in pharmaceutical, healthcare, or life sciences industry (preferred).
  • Knowledge of streaming technologies, MLOps tools, and data lakehouse architecture (preferred).

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