MongoDB
About this role
MongoDB's Data Pipelines Engineering team builds the ETL that populates the company's internal data platform, the one its own analytics run on. The team extracts large datasets and makes them queryable efficiently, and is also building a generative AI framework so other teams inside MongoDB can use that data in retrieval augmented applications. This role is data engineering rather than database engineering: designing and maintaining ETL and ELT pipelines in Python and Spark across batch, file based and streaming architectures, modelling large datasets in Parquet, JSON, Avro, Iceberg and Hive, and deploying on AWS with some GCP. You would work with data analysts and scientists to deliver the datasets their work depends on, and with security and compliance on permissions. The posting publishes a clear ramp: a first pipeline in three months, ownership of a large project by six.
Who this is for
What the posting requires:
- 4+ years building ETL pipelines for a data lake or warehouse.
- Expertise in Python, Spark, SQL and Airflow.
- Experience with data warehousing and engineering concepts, analytical data modelling, data quality validation, monitoring and pipeline reliability practices.
- Hive, Iceberg, Glue or other technologies that expose big data as tables.
- Familiarity with big data file formats including Parquet, Avro and JSON.
- A background building data platforms on cloud, for example AWS, GCP or Azure.
Nice to have:
- Exposure to real time or streaming data technologies.
What the work actually looks like:
- Design, build and maintain efficient, scalable ETL and ELT pipelines using Python and Spark across batch, file based and streaming architectures.
- Keep data quality, reliability and timeliness in line with established data engineering practice.
- Model and store large datasets using modern file and table formats.
- Deploy pipelines on cloud infrastructure, mostly AWS with some GCP.
- Partner with data analysts and data scientists to deliver the datasets that drive their work.
- Work with security and compliance teams so datasets carry the right permissions.
- Work with the Data Platform and Governance teams to keep data scalable, consumable and discoverable.
The ramp the employer publishes:
- In 3 months: you will have built your first ETL pipeline with stakeholders in data analytics and data science.
- In 6 months: you will have owned a large project from scoping and design through delivery.
- In 12 months: you will have designed new features, led development work and become a go to expert on parts of the system.
Location and working pattern: Gurgaon, and the posting states directly that MongoDB is looking for candidates based in Gurgaon for its hybrid working model. So this is hybrid, not remote, and location matters to them.
Honest fit guidance: at 4+ years this is the lowest floor of the three MongoDB roles on today's board. The requirement list is squarely data engineering, so a backend engineer without Spark and Airflow depth would be applying against the grain.
- 4+ years building ETL pipelines for a data lake or warehouse.
- Expertise in Python, Spark, SQL and Airflow.
- Experience with data warehousing and engineering concepts, analytical data modelling, data quality validation, monitoring and pipeline reliability practices.
- Hive, Iceberg, Glue or other technologies that expose big data as tables.
- Familiarity with big data file formats including Parquet, Avro and JSON.
- A background building data platforms on cloud, for example AWS, GCP or Azure.
Nice to have:
- Exposure to real time or streaming data technologies.
What the work actually looks like:
- Design, build and maintain efficient, scalable ETL and ELT pipelines using Python and Spark across batch, file based and streaming architectures.
- Keep data quality, reliability and timeliness in line with established data engineering practice.
- Model and store large datasets using modern file and table formats.
- Deploy pipelines on cloud infrastructure, mostly AWS with some GCP.
- Partner with data analysts and data scientists to deliver the datasets that drive their work.
- Work with security and compliance teams so datasets carry the right permissions.
- Work with the Data Platform and Governance teams to keep data scalable, consumable and discoverable.
The ramp the employer publishes:
- In 3 months: you will have built your first ETL pipeline with stakeholders in data analytics and data science.
- In 6 months: you will have owned a large project from scoping and design through delivery.
- In 12 months: you will have designed new features, led development work and become a go to expert on parts of the system.
Location and working pattern: Gurgaon, and the posting states directly that MongoDB is looking for candidates based in Gurgaon for its hybrid working model. So this is hybrid, not remote, and location matters to them.
Honest fit guidance: at 4+ years this is the lowest floor of the three MongoDB roles on today's board. The requirement list is squarely data engineering, so a backend engineer without Spark and Airflow depth would be applying against the grain.
Apply on company site
Opens www.mongodb.com, the employer's own application page. Applying is always free.