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Salary
≈ $10k – $28k per year (Estimated)
Location
Remote (India)
Seniority
Junior · 2+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Aug 26, 2026. Onehouse scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Onehouse provides a managed data lakehouse built on Apache Hudi, the table format its founder created at Uber. Established in 2021 in California, it keeps data in open formats so that any engine, including machine learning frameworks, can read it. The service automates ingestion, compaction and optimisation.

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. The Data Infrastructure team is the grounding heartbeat to all of this. We live and breathe databases, building cornerstone infrastructure by working under Hudi's hood to solving incredibly complex optimization and systems problems.

The Impact You Will Drive:

  • As a foundational member of the Data Infrastructure team, you will productionize the next generation of our data tech stack by building the software and data features that actually process all of the data we ingest.
  • Accelerate our open source <> enterprise flywheel by working on the guts of Apache Hudi's transactional engine and optimizing it for diverse Onehouse customer workloads.
  • Act as a SME to deepen our teams' expertise on database internals, query engines, storage and/or stream processing.

A Typical Day:

  • Design new concurrency control and transactional capabilities that maximize throughput for competing writers.
  • Design and implement new indexing schemes, specifically optimized for incremental data processing and analytical query performance.
  • Design systems that help scale and streamline metadata and data access from different query/compute engines.
  • Solve hard optimization problems to improve the efficiency (increase performance and lower cost) of distributed data processing algorithms over a Kubernetes cluster.
  • Leverage data from existing systems to find inefficiencies, and quickly build and validate prototypes.
  • Collaborate with other engineers to implement and deploy, safely rollout the optimized solutions in production.

What You Bring to the Table:

  • Strong, object-oriented design and coding skills (Java and/or C/C++ preferably on a UNIX or Linux platform).
  • Experience with inner workings of distributed (multi-tiered) systems, algorithms, and relational databases.
  • You embrace ambiguous/undefined problems with an ability to think abstractly and articulate technical challenges and solutions.
  • An ability to prioritize across feature development and tech debt with urgency and speed.
  • An ability to solve complex programming/optimization problems.
  • An ability to quickly prototype optimization solutions and analyze large/complex data.
  • Robust and clear communication skills.
  • Nice to haves (but not required):
  • Experience working with database systems, Query Engines or Spark codebases.
  • Experience in optimization mathematics (linear programming, nonlinear optimization).
  • Existing publications of optimizing large-scale data systems in top-tier distributed system conferences.
  • PhD degree with 2+ years industry experience in solving and delivering high-impact optimization projects.
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