This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior QA Engineer – Data & Analytics based in Brazil.
This is a hands-on senior QA role focused on ensuring the accuracy, reliability, and integrity of data products across complex, large-scale environments.
You will work across data pipelines, lakes, warehouses, migrations, analytics platforms, and downstream reporting.
The role partners closely with Data Engineers, Analysts, Developers, and other stakeholders to embed quality throughout the data lifecycle.
You will define validation strategies, investigate discrepancies, and help establish strong data quality and observability practices.
SQL will be central to reconciliation, troubleshooting, and end-to-end validation of data transformations and integrations.
You will also contribute to automation, performance testing, governance, security, and cloud data platform initiatives.
This is an opportunity to influence how business-critical data is trusted, monitored, and delivered within an Agile international environment.
Accountabilities:
- Define and implement data quality and validation strategies across data pipelines, data lakes, warehouses, and analytics platforms.
- Test data throughout its lifecycle, validating accuracy, completeness, consistency, integrity, freshness, and reliability.
- Validate ETL/ELT pipelines, transformations, integrations, and data flows from source systems through downstream analytics.
- Develop and execute end-to-end data validation, reconciliation, exploratory testing, and discrepancy-investigation activities using SQL extensively.
- Collaborate with engineering teams on data contracts, schema validation, anomaly detection, and automated quality controls.
- Support data quality automation through frameworks such as Great Expectations or equivalent solutions.
- Validate BI and analytics outputs to ensure reports and insights accurately represent the underlying data.
- Support large-scale data migration testing, helping protect data integrity and business continuity during cloud or platform transitions.
- Define and monitor validation criteria, data freshness SLAs, quality metrics, and other indicators of data reliability.
- Contribute to data observability, lineage, governance, monitoring, security, privacy, and compliance initiatives.
- Investigate recurring data quality issues, identify root causes, and partner with engineering teams to implement preventative improvements.
- Support performance and scalability testing across data platforms, including query performance, reliability, and large-volume processing.
- Work within an Agile, cross-functional environment while advocating for quality and challenging assumptions when necessary.
Requirements:
- Strong professional experience in QA for data-focused projects, rather than primarily application or UI testing.
- Hands-on experience testing data pipelines, ETL/ELT processes, data transformations, and integrations.
- Strong SQL skills with practical experience in data validation, reconciliation, troubleshooting, and root-cause analysis.
- Solid understanding of data engineering workflows and data lake/data warehouse architectures.
- Experience testing APIs, integrations, and data flows in cloud environments such as GCP, AWS, or Azure.
- Experience with data quality frameworks such as Great Expectations or similar tools.
- Experience validating data across multiple systems and environments and investigating inconsistencies between them.
- Understanding of BI and analytics validation, ideally involving platforms such as ThoughtSpot, Power BI, Looker, or Tableau.
- Strong analytical, problem-solving, investigative, and attention-to-detail skills.
- Excellent communication and collaboration skills, with the ability to work effectively with Data Engineers, Analysts, Developers, and business stakeholders.
- A proactive quality mindset and the confidence to promote data quality throughout the development lifecycle.
- Experience working in Agile and cross-functional teams.
- Strong written and spoken English communication skills.
- Experience with large-scale cloud or data-platform migrations, particularly Databricks-to-GCP environments, is highly desirable.
- Experience with Databricks and/or GCP, data observability, lineage tracking, monitoring, performance testing, governance, security, or privacy is a plus.
- Exposure to machine-learning model validation or AI testing is advantageous.
Benefits:
- Remote work from Brazil or other approved locations.
- Collegial and collaborative international working environment.
- Shared responsibility and autonomy, with opportunities to contribute to the team culture.
- Agile environment where ideas, initiative, and continuous improvement are encouraged.
- Opportunities to work on different projects and expand your technical experience.
- Ongoing training and mentoring.
- Opportunities for professional growth and development.
- Potential opportunities to travel.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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