Manager, Data Engineering
Pfizer's Global Commercial Analytics team is looking for a Manager, Data Engineering to build data foundations for AI and analytics applications. You will collaborate with data scientists to develop data models, pipelines, and feature stores for complex AI and RAG use cases in the commercial pharma domain. The role requires extensive experience with the modern data stack, including Python, Snowflake, and dbt, along with strong project leadership skills. This position is a hybrid role based in Mumbai focused on delivering high-impact strategic data solutions.
50k new jobs listed every day. Install TAL to find more jobs like this.
Experience
6-9 years
Function
Engineering
Work mode
Hybrid, India
Company
Tier 2
What you will work on
Pfizer's Global Commercial Analytics team is looking for a Manager, Data Engineering to build data foundations for AI and analytics applications. You will collaborate with data scientists to develop data models, pipelines, and feature stores for complex AI and RAG use cases in the commercial pharma domain. The role requires extensive experience with the modern data stack, including Python, Snowflake, and dbt, along with strong project leadership skills. This position is a hybrid role based in Mumbai focused on delivering high-impact strategic data solutions.
TAL's take
High-impact role at a major global pharmaceutical firm focusing on modern AI/RAG data pipelines and strategic analytics.
The JD is very clear, defining the team, the mission (AI/RAG, ROI analysis), and a precise, modern technical stack.
Salaries at Pfizer
22.3 LPA average
Based on 4 Grapevine salary entries for Pfizer.
Other roles
2 - 4 years | 1000000-1200000
9 LPA average
Range: 9 - 9 LPA
Other roles
4 - 6 years | L1
5 LPA average
Range: 5 - 5 LPA
Other roles
6 - 8 years | Manager
35 LPA average
Range: 35 - 35 LPA
Other roles
8 - 10 years
40 LPA average
Range: 40 - 40 LPA
Must haves
- 6-9 years of experience in data or analytics engineering
- Strong Python skills with libraries like Polars, Pandas, and Numpy
- Hands-on experience with dbt, Airflow, Spark, and Snowflake
- Background in building and managing complex data models and warehouses
- Experience with SQL and NoSQL databases
- Proficiency with Git, CI/CD, and Docker
Tools and skills
Nice to have: tableau, power bi, streamlit.
About the company
Global pharmaceutical company with significant internal data engineering operations but not a pure-play tech firm.