Data Quality Engineer

We are seeking a Data Quality Engineer to support the conversion and validation of data received from a third-party vendor. This role focuses on ensuring data accuracy, reliability, and completeness through automated testing, defect remediation, and script validation. The ideal candidate will have strong SQL and Python (or dbt) skills, experience working with ETL pipelines, and a quality-focused mindset with solid development capabilities.

• Convert and validate data provided by a third-party vendor to align with internal data standards and requirements
• Design, build, and execute automated data quality tests to ensure data accuracy and integrity
• Analyze and remediate defects found in third-party vendor code, applying development best practices
• Validate datasets by running and reviewing converted scripts and workflows
  
  Collaborate with data engineers, analysts, and business stakeholders to resolve data quality issues
• Monitor data pipelines and proactively identify potential data defects or inconsistencies
• Document test cases, defects, and validation results using automated testing or test management tools

• 3–5 years of experience in data quality engineering, data engineering, or a related role
• Strong proficiency in SQL
• Hands‑on experience with Python or dbt
• Experience validating data within ETL pipelines
• Background in automated testing and test management tools (e.g., X-Ray, Test Rail, Zephyr, or similar)
• Ability to debug and remediate code issues, including code delivered by third‑party vendors
• Strong analytical skills and attention to detail
• Experience with at least one modern data or BI platform, such as Snowflake, Databricks, Power BI or Tableau
• Familiarity with data warehousing concepts and cloud‑based data ecosystems
• Experience working in Agile or Scrum environments

Mid‑Senior level

Contract

• Information Technology
• IT Services and IT Consulting and Banking

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