Software Development Engineer III Data

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.

What You’ll Do:
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.

What We’re Looking For:
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.

Good to Have:
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