Job description

The Data QA / ETL Test Engineer will own end-to-end data testing activities for large-scale data platforms supporting P&C insurance solutions. This role requires strong hands-on experience in ETL testing, data validation, and automation, along with the ability to work closely with onsite stakeholders and offshore teams. The candidate is expected to take ownership of data quality, testing strategy, defect triage, and communication across teams. Key Responsibilities • Own onsite data QA activities, acting as the primary point of contact for data testing and quality assurance. • Validate large datasets across source-to-target systems, ensuring accuracy, completeness, and integrity. • Develop, review, and execute ETL test plans, test cases, and test scenarios covering functional, integration, regression, and performance testing. • Perform thorough data validation on ETL pipelines including extraction, transformation, and loading processes. • Write and execute complex SQL queries for data analysis, reconciliation, and validation. • Test and analyse batch job runs, review logs, and troubleshoot data and ETL failures. • Perform root cause analysis (RCA) for data defects and ETL issues, and work with data engineering teams to drive resolution. • Automate data validation and testing processes using SQL, Python, or similar scripting tools to improve efficiency and coverage. • Ensure data flows correctly through various stages of the pipeline and meets defined quality standards. • Collaborate closely with data engineers, architects, business analysts, and offshore QA teams to clarify requirements and prioritize defects. • Review offshore test execution, provide guidance, and ensure alignment with onsite expectations. • Document test plans, test results, data quality issues, and communicate findings clearly to both technical and non-technical stakeholders. • Drive continuous improvement in data quality processes, tools, and testing methodologies. • Ensure adherence to Agile practices, quality standards, and best practices in an onsite–offshore delivery model. Must-Have Skills & Experience • Hands-on experience in ETL and data testing. • Strong understanding of ETL processes, data migrations, and data warehousing concepts. • Advanced proficiency in SQL, including writing complex queries for data validation. • Hands-on experience with Hive, Spark, Spark SQL, and DataFrame APIs. • Experience working with Snowflake data warehouse. • Experience testing data pipelines built on modern big data platforms. • Knowledge of ETL tools such as IDMC / IICS and familiarity with Informatica-based ecosystems. • Experience with data quality automation tools and frameworks. • Strong troubleshooting skills, including log analysis and RCA for data and ETL issues. • Experience with defect tracking and test management tools such as Jira or Client ALM. • Solid understanding of regression testing in data-centric systems. • Good understanding of Property & Casualty (P&C) insurance data, models, and workflows is a strong advantage. Soft Skills & Ownership Expectations • Strong ownership mindset with the ability to drive tasks to closure independently. • Excellent communication skills, especially in an onsite role coordinating with multiple stakeholders. • Ability to clearly document and present findings, risks, and recommendations. • Strong analytical and problem-solving skills with attention to detail. • Proven experience working in Agile teams within an onsite–offshore model. • Collaborative team player who can mentor offshore team members and set clear expectations.

Requirements

Must-haves

Own onsite data QA activities, acting as the primary point of contact for data testing and quality assurance.
Validate large datasets across source-to-target systems, ensuring accuracy, completeness, and integrity.
Develop, review, and execute ETL test plans, test cases, and test scenarios covering functional, integration, regression, and performance testing.
Perform thorough data validation on ETL pipelines including extraction, transformation, and loading processes.
Write and execute complex SQL queries for data analysis, reconciliation, and validation.
Test and analyse batch job runs, review logs, and troubleshoot data and ETL failures.
Perform root cause analysis (RCA) for data defects and ETL issues, and work with data engineering teams to drive resolution.
Automate data validation and testing processes using SQL, Python, or similar scripting tools to improve efficiency and coverage.
Ensure data flows correctly through various stages of the pipeline and meets defined quality standards.
Collaborate closely with data engineers, architects, business analysts, and offshore QA teams to clarify requirements and prioritize defects.
Review offshore test execution, provide guidance, and ensure alignment with onsite expectations.
Document test plans, test results, data quality issues, and communicate findings clearly to both technical and non-technical stakeholders.
Drive continuous improvement in data quality processes, tools, and testing methodologies.
Ensure adherence to Agile practices, quality standards, and best practices in an onsite–offshore delivery model.
Must-Have Skills & Experience
Hands-on experience in ETL and data testing.
Strong understanding of ETL processes, data migrations, and data warehousing concepts.
Advanced proficiency in SQL, including writing complex queries for data validation.
Hands-on experience with Hive, Spark, Spark SQL, and DataFrame APIs.
Experience working with Snowflake data warehouse.
Experience testing data pipelines built on modern big data platforms.
Knowledge of ETL tools such as IDMC / IICS and familiarity with Informatica-based ecosystems.
Experience with data quality automation tools and frameworks.
Strong troubleshooting skills, including log analysis and RCA for data and ETL issues.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

sqlpythonagilejira

Platform: jobdiva
Effort: medium