Data Quality Engineer

Requirements

Key Responsibilities

• Analyze business and technical requirements to design, develop, and execute comprehensive test plans for ETL pipelines and data transformations
• Perform data validation, reconciliation, and integrity checks across various data sources and target systems
• Build and automate data quality checks using SQL and/or Python scripting
• Identify, document, and track data quality issues, anomalies, and defects.
• Collaborate with data engineers, developers, QA, and business stakeholders to understand data requirements and ensure data quality standards are met
• Define data quality KPIs and implement continuous monitoring frameworks
• Participate in data model reviews and provide input on data quality considerations
• Perform root cause analysis for data discrepancies and work with teams to drive resolution
• Ensure alignment to data governance policies, standards, and best practices.
  

Qualifications Required

• Bachelor's degree in Computer Science, Information Technology, or a related field
• 4 to 7 years of experience as a Data Quality Engineer, ETL Tester, or a similar role
• Strong understanding of ETL concepts, data warehousing principles, and relational database design
• Proficiency in SQL for complex querying, data profiling, and validation tasks.
• Familiarity with data quality tools and testing methodologies
• Excellent analytical and problem-solving skills with a strong attention to detail
• Ability to work independently and collaboratively in a fast-paced environment.
  

Preferred

• Experience with data quality frameworks (e.g., Soda, Great Expectations, Deequ, Monte Carlo, DBT Test)
• Experience with modern cloud data ecosystems (AWS, Snowflake, Apache Spark, Redshift)
• Advanced knowledge of SQL, including the ability to write stored procedures, triggers, analytic/windowing functions, and performance tuning.
• Familiarity with data pipeline tools like Airflow, DBT, or Informatica
• Experience integrating data validation processes into CI/CD pipelines using tools like GitHub Actions, Jenkins, or similar
• Background to big data platforms, data lakes, or non-relational databases.
• Understanding of data lineage, and master data management (MDM) concepts
• Experience with Agile/Scrum development methodologies