About this role
When 5% of Indian households shop with us, it’s important to build data-backed, resilient systems to manage millions of orders every day. We’ve done this – with zero downtime! 😎 Sounds impossible? Well, that’s the kind of Engineering muscle that has helped Meesho become the e-commerce giant that it is today. We value speed over perfection, and see failures as opportunities to become better. We’ve taken steps to inculcate a strong ‘Founder’s Mindset’ across our engineering teams, making us grow and move fast. We place special emphasis on the continuous growth of each team member - and we do this with regular 1-1s and open communication. Tech Culture We have a unique tech culture where engineers are seen as problem solvers. The engineering org is divided into multiple pods and each pod is aligned to a particular business theme. It is a culture driven by logical debates & arguments rather than authority. At Meesho, you get to solve hard technical problems at scale as well as have a significant impact on the lives of millions of entrepreneurs. You are expected to contribute to the Solutioning of product problems as well as challenge existing solutions. Meesho’s user base has grown 4x in the last 1 year and we have more than 50 million downloads of our app. Here are a few projects we have completed last year to scale oursystems for this growth: ● We have developed API gateway aggregators using frameworks like Hystrix and spring-cloud-gateway for circuit breaking and parallel processing. ● Our serving microservices handle more than 15K RPS on normal days and during saledays this can go to 30K RPS. Being a consumer app, these systems have SLAs of ~10ms ● Our distributed scheduler tracks more than 50 million shipments periodically fromdifferent partners and does async processing involving RDBMS. ● We use an in-house video streaming platform to support a wide variety of devices and networks. When 5% of Indian households shop with us, it’s important to build data-backed, resilient systems to manage millions of orders every day. We’ve done this – with zero downtime! 😎 Sounds impossible? Well, that’s the kind of Engineering muscle that has helped Meesho become the e-commerce giant that it is today. We value speed over perfection, and see failures as opportunities to become better. We’ve taken steps to inculcate a strong ‘Founder’s Mindset’ across our engineering teams, making us grow and move fast. We place special emphasis on the continuous growth of each team member - and we do this with regular 1-1s and open communication. Tech Culture We have a unique tech culture where engineers are seen as problem solvers. The engineering org is divided into multiple pods and each pod is aligned to a particular business theme. It is a culture driven by logical debates & arguments rather than authority. At Meesho, you get to solve hard technical problems at scale as well as have a significant impact on the lives of millions of entrepreneurs. You are expected to contribute to the Solutioning of product problems as well as challenge existing solutions. Meesho’s user base has grown 4x in the last 1 year and we have more than 50 million downloads of our app. Here are a few projects we have completed last year to scale oursystems for this growth: ● We have developed API gateway aggregators using frameworks like Hystrix and spring-cloud-gateway for circuit breaking and parallel processing. ● Our serving microservices handle more than 15K RPS on normal days and during saledays this can go to 30K RPS. Being a consumer app, these systems have SLAs of ~10ms ● Our distributed scheduler tracks more than 50 million shipments periodically fromdifferent partners and does async processing involving RDBMS. ● We use an in-house video streaming platform to support a wide variety of devices and networks. Design and implement scalable and fault-tolerant data pipelines (batch and streaming) using frameworks like Apache Spark , Flink , and Kafka . Lead the design and development of data platforms and reusable frameworks that serve multiple teams and use cases. Build and optimize data models and schemas to support large-scale operational and analytical workloads. Deeply understand Apache Spark internals and be capable of modifying or extending the open-source Spark codebase as needed. Develop streaming solutions using tools like Apache Flink , Spark Structured Streaming . Drive initiatives that abstract infrastructure complexity , enabling ML, analytics, and product teams to build faster on the platform. Champion a platform-building mindset focused on reusability , extensibility , and developer self-service . Ensure data quality, consistency, and governance through validation frameworks, observability tooling, and access controls. Optimize infrastructure for cost, latency, performance , and scalability in modern cloud-native environments . Mentor and guide junior engineers , contribute to architecture reviews, and uphold high engineering standards. Collaborate cross-functionally with product, ML, and data teams to align technical solutions with business needs. 5-8 years of professional experience in software/data engineering with a focus on distributed data systems . Strong programming skills in Java , Scala , or Python , and expertise in SQL . At least 2 years of hands-on experience with big data systems including Apache Kafka , Apache Spark/EMR/Dataproc , Hive , Delta Lake , Presto/Trino , Airflow , and data lineage tools (e.g., Datahb,Marquez, OpenLineage). Experience implementing and tuning Spark/Delta Lake/Presto at terabyte-scale or beyond. Strong understanding of Apache Spark internals (Catalyst, Tungsten, shuffle, etc.) with experience customizing or contributing to open-source code. Familiarity and worked with modern open-source and cloud-native data stack components such as: Apache Iceberg , Hudi , or Delta Lake Trino/Presto , DuckDB , or ClickHouse,Pinot ,Druid Airflow , Dagster , or Prefect DBT , Great Expectations , DataHub , or OpenMetadata Kubernetes , Terraform , Docker Strong analytical and problem-solving skills , with the ability to debug complex issues in large-scale systems. Exposure to data security, privacy, observability , and compliance frameworks is a plus. Contributions to open-source projects in the big data ecosystem (e.g., Spark, Kafka, Hive, Airflow) Hands-on data modeling experience and exposure to end-to-end data pipeline development Familiarity with OLAP data cubes and BI/reporting tools such as Tableau, Power BI, Superset, or Looker Working knowledge of tools and technologies like ELK Stack (Elasticsearch, Logstash, Kibana) , Redis , and MySQL Exposure to backend technologies including RxJava , Spring Boot , and Microservices architecture