Senior QA Engineer – Data & Analytics

This is a fully remote position, open to applicants in Portugal, +1 more state.

📋 Description

• Develop and execute strategies for data quality and validation across data pipelines, data lakes, and warehouses.

• Assess data throughout its entire lifecycle to ensure accuracy, completeness, consistency, and integrity.

• Validate ETL/ELT pipelines and data transformations from source systems to downstream analytics platforms.

• Create and implement comprehensive data validation and reconciliation tests.

• Utilize SQL for data validation, reconciliation, and investigative purposes.

• Collaborate with engineering teams to implement data contracts, schema validation, and anomaly detection.

• Facilitate automation of data quality using frameworks like Great Expectations.

• Conduct exploratory data testing and examine discrepancies across various systems and environments.

• Verify BI and analytics outputs against the underlying data.

• Assist in large-scale data migration testing and ensure data integrity during cloud transitions.

• Establish and monitor validation criteria, data freshness SLAs, and quality metrics.

• Contribute to strategies for data observability, lineage, governance, and monitoring.

• Identify persistent data quality issues and enhance processes in collaboration with engineering teams.

• Support performance testing to assess query performance, scalability, and reliability across data platforms.

• Participate in data security, privacy, and compliance testing as necessary.

• Collaborate as part of an agile software development team alongside Data Engineers, Analysts, Software Engineers, and other stakeholders.

⛳️ Requirements

• Significant professional experience in QA focused on data-centric projects.

• Practical experience in testing data pipelines, ETL/ELT processes, and data transformations.

• Proficient SQL skills for data validation, reconciliation, and troubleshooting.

• Understanding of data engineering workflows and architectures of data lakes and warehouses.

• Experience in testing APIs, integrations, and data flows within cloud environments such as GCP, AWS, or Azure.

• Familiarity with data quality frameworks such as Great Expectations or similar methodologies.

• Experience in validating data across multiple systems and identifying the root causes of discrepancies.

• Knowledge of validating BI and analytics outputs using tools like ThoughtSpot, Power BI, Looker, or Tableau.

• Strong analytical and problem-solving abilities with a keen attention to detail.

• Excellent communication and collaboration skills with Data Engineers, Analysts, Developers, and stakeholders.

• A proactive quality mindset and a commitment to advocating for data quality throughout the development lifecycle.

• Experience working in an Agile, cross-functional team.

• Strong verbal and written communication skills in English.

• Nice to have: experience in testing large-scale cloud or data platform migrations, particularly in Databricks-to-GCP environments.

• Nice to have: familiarity with Databricks and/or GCP.

• Nice to have: understanding of data observability, lineage tracking, and monitoring.

• Nice to have: experience in performance and scalability testing for large data platforms.

• Nice to have: knowledge of best practices in data governance, security, and privacy.

• Nice to have: exposure to machine learning model validation or AI testing.

• Nice to have: experience in large-scale or business-critical data environments.

🏝️ Benefits

• Collaborative environment with shared responsibilities and authority.

• Culture emphasizing learning from mistakes rather than criticizing failures.

• Agile environment where innovative ideas are welcomed.

• Opportunities for growth and the chance to experience various projects.

• Continuous training and mentoring.

• Potential for travel.