QA Data Automation Engineer

We are looking for a QA Data Automation Engineer to join our Data Team in a dynamic and challenging role, providing critical test coverage for Orca’s data pipelines, reporting layers, and analytics solutions.

In this role, you will be responsible for validating data integrity end-to-end — from raw ingestion and transformation layers to dashboards and downstream consumers. You will design and maintain automated tests to ensure accurate, reliable, and scalable data systems.

Key Responsibilities

• Develop and maintain automated QA tests for data pipelines, transformations, and data products.
• Perform and execute manual QA testing such as functional, regression, and sanity testing of applications, dashboards, and backend systems.
• Validate data flow across the system, including: ingestion, transformations, reports/dashboards.
• Perform data quality testing (completeness, consistency, accuracy, timeliness, schema validation).
• Write and execute SQL-based tests to validate logic, joins, aggregations, metrics, and anomalies.
• Build automation frameworks and validation scripts using Python.
• Work closely with Data Engineers and Analytics/BI stakeholders to define test coverage and acceptance criteria.
• Investigate failures and data issues, providing clear RCA and actionable bug reports.
• Document test plans, test scenarios, expected results, and automation coverage.
• Track issues in Jira, including reproducible steps and supporting evidence.
• Continuously improve QA processes for better monitoring, reliability, and faster releases.
  
  

• 4+ years of QA experience, including experience with automation or data validation flows.
• QA Methodology knowledge (STP, QA cycles)
• Proven experience testing data systems (ETL/ELT pipelines, DWH, analytics platforms, BI reports).
• Strong SQL skills – ability to write complex queries for validation and troubleshooting.
• Strong Python skills – writing scripts/tests for automated validations (pytest is a plus).
• Hands-on experience working with Data Lakes / Data Warehouses such as Snowflake (preferred).
• Strong understanding of bug lifecycle management using Jira.
• High attention to detail, critical thinking, and problem-solving mindset.
• Excellent communication skills and ability to work cross-functionally in a fast-paced environment.
  
  

• Knowledge of cloud platforms (AWS).
• Experience working with large-scale datasets, partitions, and performance tuning.