Senior Data QA Developer
J
JobgetherData Engineering
Fully remote position open to candidates anywhere in Canada.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in Data QA, Data Engineering QA, or a closely related discipline
- Required Skills
- PythonSQLETLGCPAirflowCI/CDData modelingBigQuery
Requirements
- 5+ years of experience in Data QA, Data Engineering QA, or a closely related discipline, preferably within complex SaaS or cloud data environments.
- Strong Python and SQL skills, with the ability to develop clean, maintainable, and scalable automated testing solutions.
- Hands-on experience with modern data platforms, particularly GCP and BigQuery.
- Practical experience with data orchestration technologies such as Airflow.
- Strong understanding of ETL/ELT pipelines, data transformations, data validation, data modeling, and dimensional design.
- Ability to analyze complex SQL execution plans and identify data or query performance bottlenecks.
- Experience designing automated testing, data quality checks, regression testing, monitoring, or observability frameworks.
- Familiarity with Git-based CI/CD environments and data testing integration into automated deployment workflows.
- Bachelor’s or master’s degree in Computer Science, Software Engineering, or a related technical discipline.
- Experience with Pulumi or Terraform is a significant advantage.
- Familiarity with data quality and observability tools such as Great Expectations, Monte Carlo, or comparable technologies is beneficial.
Responsibilities
- Design and architect robust, automated data quality frameworks and testing suites using Python and SQL to validate data integrity, schema consistency, transformation logic, and pipeline behavior.
- Partner with Data Developers and Product Managers during solution design to establish data contracts, quality gates, acceptance criteria, and testing strategies before implementation begins.
- Develop automated data-SLA monitoring and observability capabilities to identify anomalies and data quality issues across BigQuery, Airflow, and downstream systems.
- Perform detailed validation of complex ELT/ETL processes, ensuring datasets are accurate, secure, reliable, and optimized for analytics and reporting workloads.
- Integrate data-specific automated testing and regression checks into Git-based CI/CD pipelines and infrastructure-as-code workflows.
- Investigate data defects, pipeline failures, anomalies, and SQL performance issues, identifying root causes and implementing durable solutions.
- Collaborate with engineering teams to improve data modeling, documentation, governance, development workflows, and overall quality standards.
- Contribute to continuous improvement initiatives that make data development, deployment, monitoring, and validation more efficient and reliable.
- Help establish scalable QA practices and technical standards that support the growth of a modern cloud-based data platform.
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