Senior QA Engineer – Data & Analytics
New
J
JobgetherData & Analytics
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- SQLAgileCloud ComputingETLData engineering
Requirements
- Strong professional experience in QA for data-focused projects.
- 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.
- Understanding of BI and analytics validation (e.g., ThoughtSpot, Power BI, Looker, or Tableau).
- Strong analytical, problem-solving, investigative, and attention-to-detail skills.
- Excellent communication and collaboration skills working with cross-functional teams.
- Strong written and spoken English communication skills.
- Experience with Agile methodologies.
Responsibilities
- 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.
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