Data Engineer II

About the role

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world's most innovative companies to build strong engineering teams ready for what's next. Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills. You will own the design, implementation, and reliability of data pipelines that power our modern analytics and AI features. You will collaborate closely with product and analytics teams to turn business questions into structured, scalable data solutions. You will take ownership of end to end data workflows from ingestion through transformation to consumption, ensuring clarity and correctness. You will contribute to datasets that power HackerRank for Work customers and support high impact use cases. You will work within a fast moving, AI-first environment where data quality and speed directly influence product outcomes. You will grow into broader ownership over time as you prove impact and deepen your expertise.

Key facts

What you'll do

Build and maintain data pipelines on our stack

StarRocks (OLAP), Apache Hudi (Data Lake), Trino, and Spark - under guidance from senior team members.

Support in-product data features such as exports, insights dashboards, interview analytics, and the Custom Reports interface.

Help prepare clean, structured datasets that feed into AI-powered features like natural language querying.

Implement access controls and data security policies (e.g., Apache Ranger) as defined by senior engineers.

Respond to and help reduce ad-hoc data requests from internal teams (AI platform, analytics, go-to-market) by contributing to self-service pipelines.

Write clear documentation and participate in code/design reviews.

Troubleshoot data quality and pipeline issues, escalating architectural decisions to senior engineers.

Work with streaming and batch patterns to ensure timely data availability for reporting and product features.

Optimize query performance and data layouts to meet latency and concurrency targets.

Partner with data scientists and analysts to align datasets with modeling and experimentation needs.

Maintain operational runbooks and monitoring for data pipelines to support reliability and incident response.

Contribute to roadmap discussions by translating product requirements into data requirements and implementation plans.

Requirements

2-4 years of data engineering experience in similar technical roles.

Working knowledge of at least one OLAP database such as StarRocks, ClickHouse, Druid, or similar platforms.

Some experience with data lake technologies such as Hudi, Iceberg, or Delta Lake, with willingness to learn deeper details as needed.

Familiarity with distributed query engines like Trino/Presto and Apache Spark, or strong SQL and Python fundamentals with eagerness to pick up these technologies quickly.

Basic understanding of data security and access control concepts including authentication, authorization, and data governance.

Comfortable working in an AWS and open source software environment, or ability to ramp up on these tools efficiently.

Good communication skills to explain technical work to teammates and to ask for help when scoping problems is unclear.

Ability to work independently and collaboratively in a fast paced, high standards environment.

Willingness to learn new tools, frameworks, and best practices as the data stack evolves.

Attention to detail and ownership of end to end delivery in a production setting.

Nice to have

Any exposure to AI or LLM adjacent data work, such as projects or coursework involving RAG, vector stores, or LLM pipelines.

Interest in how data products are consumed by non technical, end customer facing features.

Experience working at a SaaS or B2B product company.

Practical notes

This role is hybrid in Bangalore, India.

Please refer to the source for engagement details as compensation specifics are not provided in the source material.