Data Platform Engineer (India)

About Onehouse

Onehouse is a mission-driven company dedicated to freeing data from data platform lock-in. We deliver the industry’s most interoperable data lakehouse through a cloud-native managed service built on Apache Hudi. Onehouse enables organizations to ingest data at scale with minute-level freshness, centrally store it, and make available to any downstream query engine and use case (from traditional analytics to real-time AI / ML).

We are a team of self-driven, inspired, and seasoned builders that have created large-scale data systems and globally distributed platforms that sit at the heart of some of the largest enterprises out there including Uber, Snowflake, AWS, Linkedin, Confluent and many more. Riding off a fresh $35M Series B backed by Craft, Greylock and Addition Ventures, we're now at $68M total funding and looking for rising talent to grow with us and become future leaders of the team. Come help us build the world's best fully managed and self-optimizing data lake platform!

The Community You Will Join

When you join Onehouse, you're joining a team of passionate professionals tackling the deeply technical challenges of building a 2-sided engineering product. Our engineering team serves as the bridge between the worlds of open source and enterprise: contributing directly to and growing Apache Hudi (already used at scale by global enterprises like Uber, Amazon, ByteDance etc) and concurrently defining a new industry category - the transactional data lake.

A Typical Day:

• Be the thought leader around all things data engineering within the company - schemas, frameworks, data models.
• Implement new sources and connectors to seamlessly ingest data streams.
• Building scalable job management on Kubernetes to ingest, store, manage and optimize petabytes of data on cloud storage.
• Optimize Spark applications to flexibly run in batch or streaming modes based on user needs, optimize latency vs throughput.
• Tune clusters for resource efficiency and reliability, to keep costs low, while still meeting SLAs