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Addison Group • Coppell, Texas, United States
Salary: USD 115,000–130,000 per year
Role & seniority
Stack/tools
Databricks (PySpark, Spark, Scala)
SQL/T-SQL; ETL/ELT testing
Power BI (enterprise reporting QA)
Cloud: Azure or AWS
CI/CD: ADO (Azure DevOps) or Terraform
Data pipeline architecture and governance concepts
Top 3 responsibilities
Lead full QA data lifecycle in enterprise environments: data ingestion, transformation, loading, validation, and data integrity checks
Validate and monitor data pipelines for drift, schema changes, and transformation issues; automate QA around data models/pipelines
Govern data QA practices and support standardized data best-practices; collaborate with business and technical teams; QA automation for enterprise reporting tools
Must-have skills
6+ years in Data QA Engineering
Experience with Databricks (PySpark/Scala) and data pipeline QA (ETL/ELT)
Strong SQL/T-SQL; familiarity with data governance concepts
Cloud experience (Azure or AWS)
QA automation/testing of data models and pipelines
Understanding of CI/CD processes and tooling
Nice-to-haves
Data governance/domain knowledge
Data architecture knowledge (data modeling, data warehousing)
Industry-specific experience
Power BI experience beyond testing
Location & work type
Coppell, TX
Full-time on-site role (business operations and collaboration with cross-functional teams)
Title: QA Engineer
Location: Coppell, TX
Salary Range: $115K-$130K
We are looking to bring on a QA Engineer due to new growth within our organization. This person will need to bring 6+ years of QA Engineering experience to make an immediate impact to the new business initiatives we have laid out for the next year. The focus on this team though is to specialize in running the QA lifecycle and setting processes around their data environment. QA Engineering typically helps with automation/testing of operations or applications, this team is focusing on our data management on their metadata and enterprise reporting tools.
We will need someone with experience running full QA data lifecycles in enterprise environments, specifically working within Databricks and Azure. This will include data integrity, validation of data ingestion, transformation, and loading processes (ETL/ELT). They will also need experience in monitoring pipelines (SQL) to identify metadata data drifts, schema changes, and/or transformation issues. This team will also be engineering QA automation/testing around the enterprise reporting tools (Power BI) for the business as well. Governance and setting standardized process around our data best-practices will also be key.
Tools needed for this role will include Databricks (PySpark/Scala) for automation, CI/CD tools (ADO or Terraform), SQL/T-SQL, ETL testing, and working in a cloud environment (Azure or AWS). Any certifications or data architecture knowledge (data modeling, data warehousing, data design) is a plus.
Personality-wise we need someone who can work well within a team while also completing solo tasks assigned. Collaboration between functional and technical individuals will also be day-to-day on projects, so we need someone who can partner with all aspects of the business.
6+ Years of Data QA Engineering Databricks (PySpark, Spark, Scala) QA automation/testing of data models and pipelines (ETL, ELT, data processing, etc.) Cloud-based environments (Azure or AWS) Data Pipeline Architecture and CI/CD methodology SQL
Data Governance Industry experience