Data Quality Engineer

Jobtailor; Alabama; On-site; USD 90,000 - 130,000; Full time

Job summary

Jobtailor is seeking an experienced Data Testing Engineer to design and implement robust data quality testing across enterprise data pipelines at scale. You will build automated testing frameworks using SQL, Informatica, IICS, Snowflake and Python to ensure accuracy, completeness, and reliability of reporting datasets.

You will collaborate with data engineers, analysts, QA, and business stakeholders to define testing requirements, optimize validation processes, and embed testing into CI/CD

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or a related field; equivalent work experience considered.

Responsibilities

• Design and Develop Data Testing Solutions.
• Build, maintain, and optimize automated data testing frameworks and validation pipelines for enterprise reporting using SQL, Informatica, IICS, Snowflake, and Python.
• Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.
• Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.
• Partner with data engineering teams to embed testing and quality controls into ETL/ELT pipelines and CI/CD deployment processes across Snowflake, Oracle, and AWS environments.
• Leverage AI-assisted development tools and intelligent automation techniques to improve test coverage, accelerate validation processes, and enhance the efficiency of data quality engineering practices across enterprise data platforms.
• Support and contribute to enterprise test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
• Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes.
• Work with structured and semi-structured data formats (XML, JSON) and cloud-native services to validate data ingestion, transformation, and integration processes across distributed platforms.
• Collaborate with data engineers, analysts, QA teams, and business stakeholders to define testing requirements, improve data quality processes, and support reporting solutions such as Power BI.
• Recommend and implement improvements to data quality frameworks, testing automation, monitoring solutions, governance processes, and DataOps practices.
• Mentor junior team members and promote best practices in data quality engineering and testing.

Skills

SQL development

Query tuning

Automated testing

Python automation

Data quality

ETL/ELT testing

CI/CD

Data governance

Data profiling

Test environment

Mentoring

AI-assisted testing

Education

Bachelor’s degree in Computer Science / Information Systems

Tools

Informatica

IICS

Snowflake

Oracle

AWS

Python

Job description

Responsibilities

• Design and Develop Data Testing Solutions
• Build, maintain, and optimize automated data testing frameworks and validation pipelines that support enterprise reporting, analytics, and business applications using SQL, Informatica, IICS, Snowflake, and Python.
• Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.
• Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.
• Partner with data engineering teams to embed testing and quality controls into ETL/ELT pipelines and CI/CD deployment processes across Snowflake, Oracle, and AWS environments.
• Leverage AI-assisted development tools and intelligent automation techniques to improve test coverage, accelerate validation processes, and enhance the efficiency of data quality engineering practices across enterprise data platforms.
• Support and contribute to enterprise test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
• Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes.
• Work with structured and semi-structured data formats (XML, JSON) and cloud-native services to validate data ingestion, transformation, and integration processes across distributed platforms.
• Collaborate with data engineers, analysts, QA teams, and business stakeholders to define testing requirements, improve data quality processes, and support reporting solutions such as Power BI.
• Recommend and implement improvements to data quality frameworks, testing automation, monitoring solutions, governance processes, and DataOps practices.
• Mentor junior team members and promote best practices in data quality engineering and testing.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, or a related field; equivalent work experience considered.
• 6+ years of experience in data engineering, data testing, or database development.
• Demonstrated expertise in SQL development and query tuning.
• Automated data testing and validation methodologies.
• Informatica and IICS for ETL and data integration testing.
• Snowflake data warehouse architecture and validation.
• Oracle database systems.
• Data reconciliation and data profiling techniques.
• Data modeling, normalization, and relational design.
• Handling and validating XML and JSON data structures.
• Building data quality solutions in AWS cloud environments.
• Python-based automation and testing frameworks.
• Strong knowledge of test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
• Experience establishing and supporting end-to-end test strategies for enterprise data pipelines and distributed data platforms.
• Understanding of environment dependencies, release validation processes, and data synchronization considerations for large-scale data ecosystems.
• Experience developing automated test scripts and reusable validation frameworks.
• Strong understanding of ETL/ELT testing methodologies and end-to-end data flow validation.
• Strong problem-solving abilities and the capacity to work independently on complex technical challenges.
• Deep understanding of data security, governance, compliance, and data quality best practices.
• High degree of self-motivation, intellectual curiosity, and commitment to continuous improvement.