Quality Engineer (Data)

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

Role overview

Join a data engineering team supporting a program that manages financial and regulatory data, where you will shape quality assurance across modern data pipelines and analytics platforms. This role blends hands‑on test engineering with data engineering fundamentals, applying rigor suited to highly governed data. You will partner with engineers, analysts, scientists, and stakeholders to keep datasets accurate, complete, and trustworthy.

Responsibilities

• Develop and maintain data quality strategies, standards, documentation, and testing practices for pipelines and analytical datasets.
• Design and execute automated and manual tests covering accuracy, completeness, integrity, uniqueness, schema consistency, business rules, freshness, and statistical validity.
• Build reusable quality checks and integration or end‑to‑end tests using Python, PySpark or Spark, SQL, and Apache Airflow.
• Validate transformations across Apache Spark, Python, and AWS data services such as Amazon S3.
• Investigate anomalies, schema drift, missing data, and pipeline failures, partnering with data engineers on root cause and resolution.
• Collaborate across the SDLC to translate business and regulatory requirements into quality controls, support UAT, release validation, and production deployments.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent experience.
• 7+ years of professional experience in software quality assurance, testing, or quality engineering.
• Strong Python programming and hands‑on experience building automated tests with Pytest or a comparable framework.
• Solid grasp of data quality principles including accuracy, completeness, consistency, uniqueness, validity, and timeliness.
• Experience with relational databases, SQL, and testing data pipelines or large‑scale ETL/ELT processes.
• Experience with workflow orchestration such as Airflow and cloud‑based data platforms, preferably AWS.
• Comfort supporting Agile teams and communicating technical data issues to non‑technical stakeholders.

Nice to have

• Background testing financial, securities, regulatory, or similarly governed data.
• Familiarity with AWS services like S3, Glue, Redshift, and Athena; dbt; CI/CD pipelines; and schema evolution or drift detection.
• Experience with data lineage, metadata management, observability, or data quality frameworks.
• Federal government project experience, or certifications such as CSTE or ISTQB.

Benefits and work setup

• Remote work available, with hybrid roles noted per posting.
• Estimated salary range of $75,000 to $110,000 annually, depending on experience and skills.
• Medical, dental, and vision coverage; life insurance; short‑ and long‑term disability.
• Employee Assistance Program, 401(k) with 4% match, and a liberal PTO policy.
• Annual continuing education and wellness budgets, plus referral and performance‑based bonus programs.
• Eligibility: U.S. citizenship and ability to obtain Public Trust clearance.