Staff Data Engineer

The Data Engineering team is responsible for building ETL pipelines that populate the Internal Data Platform, which drives analytics that help the company run more efficiently. Our team builds highly performant and scalable processes that extract massive datasets and makes those datasets available for querying in an optimal way.

We are looking to speak to candidates who are based in Gurgaon for our hybrid working model.

What you’ll do

Guide the Data Engineering team on building highly performance ETL pipelines using Spark and other Big Data technologies

Help design the architecture of our Internal Data Platform to support the implementation of a robust medallion architecture

Provide thought leadership on ways to achieve infrastructure cost savings on Cloud hyperscalers

Design and build AI agents that can help automate many of the common development and support tasks that the team performs

Work with Security and Compliance teams to ensure that datasets have appropriate permissions and regulations in place

Work with our Data Platform, and Governance sibling teams to make data scalable, consumable, and discoverable

We’re looking for someone with

10+ years experience working on enterprise data lakes/warehouses

5+ years of Spark and Python experience

5+ years of direct hands-on experience working with AWS or GCP

Thorough AI knowledge, particularly with codegen tools and agentic frameworks

Hive, Iceberg, Glue, or other technologies that expose big data as tables

Familiarity with different big data file types such as Parquet, Avro, and JSON

Exposure to real-time or streaming data technologies is a plus

Success Measures

In 3 months, you'll have a thorough understanding of the architecture of MongoDB’s internal Data and AI ecosystem

In 6 months, you'll have owned the delivery of a large project from start (scoping, design) to finish (delivery)

In 12 months, you'll have designed new features, led development work, and become a go-to expert on parts of the