Data Product Quality Engineer

Healthcare Consulting MNC Nagpur, Maharashtra, Nagpur, Maharashtra, IN (On-site) 5 days ago
Full Time/Permanent
Mid-Senior level

Job Description

ETL Tester | Experience: 3-8 years

We are looking for a Data Product QA Engineer (Functional + Data Validation) with strong SQL and data validation expertise — someone who ensures that data product outputs match analytics logic and business expectations, not just that pipelines run.

The right candidate will have the ability to validate large-scale datasets (not UI testing mindset).

The role is for a Healthcare consulting MNC, so pharma domain experience will be a huge plus.

Cloud Platform: AWS

Mandatory Requirements

  • Strong SQL skills — complex joins, aggregations, and reconciliation queries on large datasets.
  • Solid understanding of ETL/ELT pipelines and the ability to validate data flow end-to-end.
  • Hands-on testing experience on Databricks, AWS Redshift, or similar large-scale data platforms.
  • Familiarity with AWS S3 as a data storage and staging layer within end-to-end pipeline validation.
  • Proven ability to validate large-scale datasets — this is a data validation role, not a UI testing one.
  • Experience in data reconciliation — comparing data product outputs vs analytics outputs to ensure logical consistency.
  • Experience supporting UAT — including country-level validation walkthroughs with business stakeholders.
  • Experience testing business rules — eligibility logic, scoring/ranking thresholds, and similar configurable logic.
  • Experience validating multi-country configurations and ensuring rule variations work as expected across geographies.
  • Experience with incremental, sprint-based releases — validating changes within agile delivery cycles.
  • Strong communication and collaboration skills — comfortable engaging with analysts, developers,

and business stakeholders.

  • A Bachelor's degree (or better) in a related field, ideally IT/Computer Science engineering background.

Good to have skills

  • Exposure to PySpark or Python-based validation scripting.
  • Familiarity with test automation frameworks for data pipelines.
  • Experience with CI/CD for data — Git workflows, automated test suites.
  • Experience in pharma/life sciences domain.

Candidates with strong experience in data quality validation, data pipeline testing, and cloud-based data platforms would be preferred.

About Us

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