Senior Data Engineer / Data Analyst Control Automation & ETL Testing

Job Title Options:

Senior Data Engineer / Data Analyst – Control Automation & ETL Testing

Location:

Wilmington, DE (Hybrid – 3 days onsite/week)

Engagement Type:

Long-Term Contract (W2)

Client:

Capital One (Former Capital One experience required)

Job Summary / Description:

We are seeking an experienced Data Analyst to join the Risk and Controls organization at Capital One. This role bridges data engineering and control execution, with a strong focus on ETL development, automated data validation, and scripted QA testing.

The ideal candidate will bring deep expertise in Python and SQL, along with hands-on experience building and monitoring production-grade ETL pipelines. This position plays a critical role in ensuring data integrity, lineage, and compliance across first-line risk controls in a highly collaborative, fast-paced environment.

Key Responsibilities:

• Design, develop, and maintain scalable ETL pipelines that transform raw data into reliable datasets supporting risk and control frameworks.

• Optimize complex SQL queries and Python-based data pipelines to improve performance and reliability across platforms such as Postgres and Snowflake.

• Integrate structured and unstructured data sources (including JSON) into a unified data layer for reporting and control execution.

• Build and maintain automated data quality and QA test suites using Python frameworks such as PyTest and Great Expectations.

• Implement data-as-code testing frameworks to proactively detect anomalies, schema drift, and data integrity issues.

• Perform unit and integration testing to validate ETL logic against business and system rules.

• Support data governance initiatives, including metadata management, technical lineage, and CI/CD deployment of data assets.

• Evaluate upstream and downstream integration points to ensure SQL logic accurately reflects system states and reporting requirements.

• Identify bottlenecks in data pipelines and implement automation solutions to eliminate manual processes.

• Partner closely with Engineering, Operations, and Risk teams to translate control requirements into technical ETL specifications.

• Communicate data risks, discrepancies, and remediation plans clearly to both technical and non-technical stakeholders.

Basic Qualifications:

• Master’s degree in a quantitative or technical discipline.

• Proven experience developing and supporting ETL pipelines in production environments.

• Expert-level proficiency in Python and SQL for data manipulation, transformation, and automated testing.

• Experience working with relational and non-relational databases (Postgres, MySQL, DynamoDB, Cassandra, or similar).

Preferred Qualifications:

• Experience building automated QA and data validation frameworks.

• Hands-on experience with AWS services such as S3, Glue, Lambda, and IAM.

• Familiarity with data orchestration tools (Airflow, Prefect) and version control systems (Git).

• Strong experience processing and transforming unstructured data (JSON) for structured analytics and reporting.