We are building a unified data platform to serve 20+ product modules, enabling ingestion, processing, and retrieval of data to power analytics & systems at scale. This role is part of the

Data Ingestion

charter under the Unified Data Platform umbrella, focused on designing and developing an extremely high-scale ingestion platform with focus on data streaming, open table based data storage, and highly scalable transformation layer. This platform is the foundation to multiple experiences within Harness including dashboard, analytics, and product utilization.

About the Role

You will participate in the design, architecture & development of this platform alongside principal engineers & architects, solving deep problems in data streaming, transformation, and extremely large scale data storage.
You will work with multiple product engineering teams in an extremely collaborative fashion providing guidance to onboard onto the data platform.
Author high level & low level design documents.
Design and implement automation of infrastructure setup using helm, terraform and similar technologies.
Work alongside Site Reliability Engineers and cross functional teams to diagnose/troubleshoot any production performance related issues

About You

Bachelor s or Master s degree in computer science
5+ years of hands-on software(backend) development experience - most of which should be in the data platform space involving distributed systems
Hands on experience building ETL pipelines with technologies like SQLMesh, DBT, or Spark Structured Streaming
Hands-on experience with query engines like Trino
Deep exposure to OLAP stores like Pinot, Druid, Clickhouse, Starrocks, Snowflake, or Databricks
Working experience with open table architecture such as Iceberg, Delta Lake or Hudi.
Experience with data streaming technologies like Flink, Spark Streaming, Kafka Streams etc
Familiarity with OLTP datastores such as MongoDB, PostgreSQL
Experience scaling and operating production data systems at 10K+ QPS
Deep working experience with Java and Python
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