QA Automation Lead (Data Engineering)- India

IndiaSenior7-10 years

About Juniper Square

Private markets are one of the largest, most complex, and most underserved corners of global finance. Our mission at Juniper Square is to unlock their full potential. We’re the Operations Partner trusted by 2,300+ GPs, unifying technology, data, and fund administration services into a single platform that helps GPs move faster, make better decisions, and scale with precision. With $300B+ under administration and 700,000+ LPs on platform, we’ve built the scale to match our ambition. And with JunieAI, our purpose-built AI platform, we’re reimagining how private markets operate, embedding intelligence across every workflow. Founder-led since 2014, backed by $350M+ in funding, and now 1,000+ employees strong, we’re building a company designed to shape the future of private markets for decades to come.

Our culture is built for people who want to do ambitious, meaningful work alongside exceptionally talented teammates. We think like owners, move with urgency, and take pride in solving hard problems that truly matter to our customers and the future of private markets. We believe the best ideas come from open debate, deep collaboration, and diverse perspectives, which is why we believe transparency is the default and feedback makes us stronger. If you’re energized by high standards, rapid growth, and the opportunity to help define a category at a pivotal moment, come join us!

Juniper Square offers employees a variety of ways to work, ranging from a fully remote experience to working full-time in one of our physical offices. We invest heavily in digital-first operations, allowing our teams to collaborate effectively across 27 U.S. states, 2 Canadian Provinces, India, Luxembourg, and England. We also have physical offices in San Francisco, New York City, Mumbai and Bangalore for employees who prefer to work in an office some or all of the time.

About Your Role

As a QA Automation Lead for Data Engineering at Juniper Square, you will be the primary owner of data quality and reliability for your project. You are expected to drive the end-to-end data testing strategy, ensuring the integrity, accuracy, and performance of our data pipelines and analytical platforms, and oversee release readiness. You will collaborate closely with data engineering, product, and data science teams to help define and drive our manual and automated testing efforts. You must be detail-oriented, passionate about data accuracy, and a strong advocate for high-quality data products and the end-user experience.

What You'll Do

Quality Roadmap & Planning: Partner with Data Engineering and Product leadership to define the data validation and automation strategy for data platform features and new architecture releases.
Backend & Pipeline Testing: Design and execute complex test cases targeting backend data systems, focusing on data integrity, distributed systems logic, data transformation consistency, and asynchronous batch or stream processing.
AI-Augmented Testing: Leverage AI-powered tools like Cursor or Augment to rapidly prototype, scaffold new test suites, diagnose failures, and generate advanced data validation test scenarios.
Data Automation Excellence: Develop, maintain, and extend scalable data automation frameworks and data quality monitoring suites by leveraging LLMs.
Governance & Standards: Establish and enforce data QA best practices, coding standards, and rigorous code review processes for the automation team. Foster a culture of technical excellence and proactive problem-solving.
Advocate for Automation: Champion an automation-first approach to data quality, minimizing reliance on manual data reconciliation, and partner with data engineering to systematically decrease manual testing effort.

Qualifications

Education: Bachelor's degree in Computer Science, Data Engineering, or equivalent professional experience.
Experience: 7–10 years in Software Quality Assurance, including demonstrated ability to lead end-to-end testing efforts across the full software and data lifecycle.
Database Engineering & SQL: Knowledge of relational databases and strong proficiency in SQL with the ability to write complex queries for data validation, reconciliation, and root cause analysis.
Data Infrastructure & Concepts: Solid understanding of data engineering concepts including data pipelines, ETL/ELT workflows, data warehouse architecture, and OLAP technologies (e.g., Redshift, Snowflake, BigQuery, or equivalent).
Programming Skills: Strong proficiency in Python including the ability to read, understand, and debug data pipeline code.
QA Automation Frameworks: Proven experience in QA automation, including designing and implementing automated test frameworks, test suites, and CI/CD-integrated testing pipelines.
AI-Augmented Development: Proactive in using AI-powered tools (e.g., Augment, Cursor, Gemini) to accelerate test authoring, assist in debugging automation scripts, and optimize data documentation workflows.
Release Ownership: Experience managing the full release cycle for data features, from scoping testing requirements to final "Go/No-Go" delivery decisions.
Soft Skills: Excellent analytical and problem-solving abilities, exceptional attention to detail, and the ability to work independently in fast-paced Agile development teams with minimal supervision.
Communication: Strong written and verbal communication skills in English
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