QA Data Engineer - Lead Tester

Job Summary

We are seeking a highly skilled QA Data Engineer to join a top financial services organization and lead quality assurance (QA) efforts across data platforms and pipelines. This role will primarily focus on manual testing, validating complex datasets, and ensuring data accuracy, completeness, and integrity across systems. As the lead tester, you will work closely with data engineers, analysts, and business stakeholders to define test strategies, lead defect triage, and validate data transformations within SQL-driven environments.

This is a hands-on position for a detail-oriented professional with deep experience in SQL, relational databases, and manual data validation. Candidates should bring a strong understanding of financial services data and be comfortable operating in a fast-paced, regulated enterprise environment.

Key Responsibilities

• Lead all QA testing efforts across multiple data projects, with a strong focus on manual data validation.



• Write and execute complex SQL queries to validate data transformations, lineage, and integrity.



• Collaborate with data engineers and business stakeholders to define acceptance criteria and ensure test coverage.



• Create, maintain, and execute detailed test plans, test cases, and test data strategies for structured and semi-structured data sources.



• Coordinate and drive defect triage meetings and ensure timely resolution of data quality issues.



• Perform root cause analysis for data inconsistencies and discrepancies.



• Champion data quality standards and best practices across the QA lifecycle.



• Partner with engineering to evolve toward more automated data validation strategies over time.

Skills

"Must Haves"

• Expert-level SQL: Ability to write complex queries for data validation and investigation.



• Manual testing expertise: Proven experience validating data in relational databases without relying on test automation tools.



• Strong understanding of data warehousing concepts, ETL processes, and data pipelines.



• Experience testing data across Snowflake, SQL Server, Oracle, or similar platforms.



• Familiarity with data modeling concepts, including normalized and dimensional models.



• Experience working with large datasets, performing joins, aggregates, and anomaly detection.



• Excellent communication and stakeholder management skills.



• Prior experience in financial services or regulated industries is strongly preferred.

"Nice to Haves"

• Familiarity with data test automation tools like dbt, Great Expectations, or custom frameworks.



• Experience with Python or scripting languages to assist in data validation.



• Exposure to data governance and metadata management tools.



• Understanding of Cloud data platforms (e.g., AWS, Azure, Google Cloud Platform).



• Agile/Scrum experience and use of JIRA or similar tools for QA tracking.



• Basic knowledge of BI tools like Tableau or Power BI for data validation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.



• 5+ years of experience in data QA, testing, or data engineering roles with a QA focus.



• Demonstrated leadership in QA testing for data-intensive applications.



• Strong attention to detail, problem-solving skills, and ability to work independently.



• Prior experience working in enterprise environments, preferably in the financial sector.

Job Summary

We are seeking a highly skilled QA Data Engineer to join a top financial services organization and lead quality assurance (QA) efforts across data platforms and pipelines. This role will primarily focus on manual testing, validating complex datasets, and ensuring data accuracy, completeness, and integrity across systems. As the lead tester, you will work closely with data engineers, analysts, and business stakeholders to define test strategies, lead defect triage, and validate data transformations within SQL-driven environments.

This is a hands-on position for a detail-oriented professional with deep experience in SQL, relational databases, and manual data validation. Candidates should bring a strong understanding of financial services data and be comfortable operating in a fast-paced, regulated enterprise environment.

Key Responsibilities

• Lead all QA testing efforts across multiple data projects, with a strong focus on manual data validation.



• Write and execute complex SQL queries to validate data transformations, lineage, and integrity.



• Collaborate with data engineers and business stakeholders to define acceptance criteria and ensure test coverage.



• Create, maintain, and execute detailed test plans, test cases, and test data strategies for structured and semi-structured data sources.



• Coordinate and drive defect triage meetings and ensure timely resolution of data quality issues.



• Perform root cause analysis for data inconsistencies and discrepancies.



• Champion data quality standards and best practices across the QA lifecycle.



• Partner with engineering to evolve toward more automated data validation strategies over time.

Skills

"Must Haves"

• Expert-level SQL: Ability to write complex queries for data validation and investigation.



• Manual testing expertise: Proven experience validating data in relational databases without relying on test automation tools.



• Strong understanding of data warehousing concepts, ETL processes, and data pipelines.



• Experience testing data across Snowflake, SQL Server, Oracle, or similar platforms.



• Familiarity with data modeling concepts, including normalized and dimensional models.



• Experience working with large datasets, performing joins, aggregates, and anomaly detection.



• Excellent communication and stakeholder management skills.



• Prior experience in financial services or regulated industries is strongly preferred.

"Nice to Haves"

• Familiarity with data test automation tools like dbt, Great Expectations, or custom frameworks.



• Experience with Python or scripting languages to assist in data validation.



• Exposure to data governance and metadata management tools.



• Understanding of Cloud data platforms (e.g., AWS, Azure, Google Cloud Platform).



• Agile/Scrum experience and use of JIRA or similar tools for QA tracking.



• Basic knowledge of BI tools like Tableau or Power BI for data validation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.



• 5+ years of experience in data QA, testing, or data engineering roles with a QA focus.



• Demonstrated leadership in QA testing for data-intensive applications.



• Strong attention to detail, problem-solving skills, and ability to work independently.



• Prior experience working in enterprise environments, preferably in the financial sector.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.



• 5+ years of experience in data QA, testing, or data engineering roles with a QA focus.



• Demonstrated leadership in QA testing for data-intensive applications.



• Strong attention to detail, problem-solving skills, and ability to work independently.



• Prior experience working in enterprise environments, preferably in the financial sector.

"Must Haves"

• Expert-level SQL: Ability to write complex queries for data validation and investigation.



• Manual testing expertise: Proven experience validating data in relational databases without relying on test automation tools.



• Strong understanding of data warehousing concepts, ETL processes, and data pipelines.



• Experience testing data across Snowflake, SQL Server, Oracle, or similar platforms.



• Familiarity with data modeling concepts, including normalized and dimensional models.



• Experience working with large datasets, performing joins, aggregates, and anomaly detection.



• Excellent communication and stakeholder management skills.



• Prior experience in financial services or regulated industries is strongly preferred.

"Nice to Haves"

• Familiarity with data test automation tools like dbt, Great Expectations, or custom frameworks.



• Experience with Python or scripting languages to assist in da