Remote Data Quality Engineer for Pipelines

capitaltg; United States; Hybrid; USD 75,000 - 110,000; Full time

Benefits offered by this job

Remote work available

Hybrid roles noted per posting

Medical, dental, and vision coverage

Life insurance

Short- and long-term disability

401(k) with 4% match

Continuing education budget

Wellness budget

Referral and performance-based bonus

Job summary

CapitalTG is seeking a senior data quality engineer to join a data engineering team responsible for governance of financial and regulatory data. You will blend hands-on test engineering with data engineering fundamentals to ensure datasets are accurate and trustworthy.

Responsibilities include developing data quality strategies, automated tests, and end-to-end data validation across Spark, Python, and AWS services.

Qualifications

• Bachelor's degree or equivalent experience in CS/IS/Engineering.
• 7+ years in software QA or quality engineering.
• Strong Python and PyTest experience.
• Data quality concepts: accuracy, completeness, timeliness.
• Experience with SQL and testing data pipelines or large ETL/ELT.

Responsibilities

• Develop and maintain data quality strategies, standards, documentation, and testing practices for pipelines and datasets.
• Design and execute automated and manual tests for accuracy, completeness, integrity, and schema consistency.
• Build reusable quality checks and tests using Python, PySpark, SQL, and Airflow.
• Validate transformations across Spark, Python, and AWS data services (S3).
• Investigate anomalies, schema drift, missing data, and pipeline failures with data engineers.
• Collaborate across the SDLC to translate requirements into quality controls and support UAT.

Skills

Python

SQL

QA experience

Data quality principles

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field

Tools

PyTest

Apache Airflow

Spark

AWS (S3, Glue, Redshift, Athena)

Job description

CapitalTG is seeking a senior data quality engineer to join a data engineering team responsible for governance of financial and regulatory data. You will blend hands-on test engineering with data engineering fundamentals to ensure datasets are accurate and trustworthy.

Responsibilities include developing data quality strategies, automated tests, and end-to-end data validation across Spark, Python, and AWS services.