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 Orcas 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.
Requirements: 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.
Nice to Have
Knowledge of cloud platforms (AWS).
Experience working with large-scale datasets, partitions, and performance tuning.
This position is open to all candidates.