MongoDB
About this role
This is the senior end of MongoDB's Data Engineering team, which builds the ETL pipelines feeding the internal data platform that the company's own analytics run on. Where the Software Engineer 3 role on this team is hands on pipeline building, this one is about direction: guiding the team on building high performance ETL with Spark and other big data technologies, and helping design the architecture of the internal data platform to support a proper medallion architecture. Two things make it more current than a standard data engineering listing. First, you are asked to provide thought leadership on achieving infrastructure cost savings on cloud hyperscalers, which is a real and measurable brief. Second, you would design and build AI agents to automate the common development and support tasks the team performs, and the requirement list asks for thorough AI knowledge including codegen tools and agentic frameworks.
Who this is for
What the posting requires:
- 10+ years of experience working on enterprise data lakes and warehouses.
- 5+ years of Spark and Python experience.
- 5+ years of direct hands on experience 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 big data file types such as Parquet, Avro and JSON.
Nice to have:
- Exposure to real time or streaming data technologies.
What the work actually looks like:
- Guide the data engineering team on building high performance ETL pipelines using Spark and other big data technologies.
- Help design the architecture of the internal data platform to support a robust medallion architecture.
- Provide thought leadership on achieving infrastructure cost savings on cloud hyperscalers.
- Design and build AI agents that automate common development and support tasks the team performs.
- Work with Security and Compliance teams so datasets carry appropriate permissions and regulatory controls.
- Work with the Data Platform and Governance sibling teams to keep data scalable, consumable and discoverable.
The ramp the employer publishes:
- In 3 months: a thorough understanding of the architecture of MongoDB's internal data and AI ecosystem.
- In 6 months: ownership of the delivery of a large project from scoping and design through to delivery.
- In 12 months: having designed new features, led development work, and become a go to expert on parts of the system.
Location and working pattern: the posting states MongoDB is looking for candidates based in Gurgaon for its hybrid working model.
Honest fit guidance: at 10 years plus this is a technical leadership seat without being a management one, and the AI agent building brief is a genuine part of the job rather than a bolt on. If your data engineering experience is deep but you have not touched agentic frameworks or codegen tooling, that gap is stated explicitly in the requirements and is worth closing before applying.
- 10+ years of experience working on enterprise data lakes and warehouses.
- 5+ years of Spark and Python experience.
- 5+ years of direct hands on experience 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 big data file types such as Parquet, Avro and JSON.
Nice to have:
- Exposure to real time or streaming data technologies.
What the work actually looks like:
- Guide the data engineering team on building high performance ETL pipelines using Spark and other big data technologies.
- Help design the architecture of the internal data platform to support a robust medallion architecture.
- Provide thought leadership on achieving infrastructure cost savings on cloud hyperscalers.
- Design and build AI agents that automate common development and support tasks the team performs.
- Work with Security and Compliance teams so datasets carry appropriate permissions and regulatory controls.
- Work with the Data Platform and Governance sibling teams to keep data scalable, consumable and discoverable.
The ramp the employer publishes:
- In 3 months: a thorough understanding of the architecture of MongoDB's internal data and AI ecosystem.
- In 6 months: ownership of the delivery of a large project from scoping and design through to delivery.
- In 12 months: having designed new features, led development work, and become a go to expert on parts of the system.
Location and working pattern: the posting states MongoDB is looking for candidates based in Gurgaon for its hybrid working model.
Honest fit guidance: at 10 years plus this is a technical leadership seat without being a management one, and the AI agent building brief is a genuine part of the job rather than a bolt on. If your data engineering experience is deep but you have not touched agentic frameworks or codegen tooling, that gap is stated explicitly in the requirements and is worth closing before applying.
Apply on company site
Opens www.mongodb.com, the employer's own application page. Applying is always free.