Sr. QA Engineer

Owns quality of data from source systems through ETL/ELT into BigQuery and the Looker reporting layer. Also covers functional and API testing of surrounding services. Embedded in an Agile/Scrum team with data engineers and analysts.

The role is not limited to a single stream. Depending on delivery priorities, the person will work across different projects, including data and reporting (primary focus), web and eCommerce, and other platform initiatives. We expect flexibility: switching context between projects, ramping up on an unfamiliar domain quickly, and applying the same structured test approach regardless of which system is under test.

We're looking for a QA role, so the primary focus must be quality/testing. Since we'll also be working with data, data/SQL skills are important, but the candidate should primarily be a QA/test engineer with additional data experience.

Responsibilities:

• Define and run data quality checks: counts, completeness, nulls, duplicates, referential integrity, field-level reconciliation, KPI, and business rule validation.
• Reconcile source vs. target during migrations; find the root cause of discrepancies.
• Validate ETL/ELT logic and the dashboards/KPIs built on top of it.
• Test APIs and integrations that feed or consume data.
• Own the defect lifecycle end-to-end with data engineers.
• Contribute to test strategy, UAT, and release readiness.
• Test design (core of the role)
• Turn business and reporting requirements into clear test scenarios, test cases, and checklists. This is a daily activity, not documentation done after the fact.
• Maintain test artifacts in Jira/Xray so results are traceable and repeatable by others.
• Build and own a smoke suite for data pipelines and reports: run after each deployment or pipeline release to confirm data loads, key tables are populated, and critical KPIs are not broken.
• Build and maintain a regression suite covering reconciliation checks, business rules, and key dashboards; keep it lean and prioritized by risk, and extend it with every confirmed defect.

Requirements:

• 6 to 8 years of experience, 5+ years in testing, and 1-2 years in data/BI/analytics.
• Confident with SQL: comfortable writing joins and aggregations and comparing datasets independently.
• Practical experience with data quality checks, reconciliation, and validation of migrated data.
• Working understanding of data models, schemas, and dependencies; structured and semi-structured data.
• Independent test design (scenarios, cases, checklists) and clear defect documentation.
• Experience building and maintaining smoke and regression coverage.
• Agile/Scrum and Git-based workflows.
• Good analytical and root-cause skills.
• Testing of data or reporting-platform migrations.
• Validation of dashboards, reports, and KPIs.
• ETL/ELT concepts and cloud data architectures.
• API testing and data profiling.
• Experience in web testing.
• Data lineage; direct work with business stakeholders.

Nice to have

• Google BigQuery.
• Basic Python for data comparison and validation.
• Experience in test automation.
• Understanding of AI/ML projects that consume data: awareness of how data quality, completeness, and consistency affect model inputs and outputs.
• Experience testing eCommerce or retail platforms: checkout, orders, stock, product, and transactional data.
• Looker or another BI platform.
• DBT/GCP Dataform tests.
• Airflow / Cloud Composer, GCP data services.
• CI/CD for data pipelines.

Skills

ELT, ETL, QA, SQL, Test, Testing