Position Details
Opening For Business Data Analyst Contract Apply
Note: Only W2 Candidates will be Considered.
Types of Work:
ETL Development & Data Engineering...
- Design, develop, and maintain robust ETL pipelines to aggregate and transform raw data into actionable datasets for control execution.
- Optimize complex SQL queries and Python scripts to improve data processing speed and reliability across various database environments (Postgres, Snowflake, etc.).
- Integrate disparate data sources-including unstructured JSON and relational warehouses-into a unified data layer for risk reporting.
Automated Data Validation & Scripted QA...
- Build and execute automated QA test suites using Python (e.g., PyTest, Great Expectations) to validate data completeness, accuracy, and timeliness.
- Develop "Data-as-Code" testing frameworks to catch anomalies or schema drift before they impact downstream control processes.
- Perform unit and integration testing on ETL code bases to ensure the logic reflects the underlying business and system rules.
Data Governance & Lineage...
- Manage data repositories and CI/CD pipelines to ensure seamless and governed deployment of data assets.
- Drive adherence to data quality principles, including automated metadata capture and technical lineage mapping.
- Evaluate integration points to ensure SQL logic accurately captures the state of the systems being reported on.
General Responsibilities:
- Pipeline Optimization: Identify bottlenecks in data delivery and implement Python-based solutions to automate manual data work.
- Technical Partnership: Collaborate with Engineering and Ops to translate control requirements into technical specifications for ETL workflows.
- Strategic Problem Solving: Use a quantitative mindset to solve data gaps, leveraging Python libraries for deep-dive analysis into data anomalies.
- Communication: Clearly articulate technical risks and data discrepancies to non-technical stakeholders to drive remediation.
Basic Qualifications:
- Master's Degree in a quantitative or technical field.
- Proven experience building and running ETL pipelines in a production environment.
- Expert-level proficiency in Python and SQL, specifically for data manipulation and automated testing.
- Experience with relational and non-relational databases (Postgres, MySQL, DynamoDB, Cassandra, or similar).
Preferred Qualifications:
- Experience building automated QA frameworks for data validation.
- Hands-on experience with AWS services (S3, Glue, Lambda, IAM) to support serverless data processing.
- Familiarity with data orchestration tools (e.g., Airflow, Prefect) and version control (Git).
- Experience handling unstructured data (JSON) and transforming it for structured reporting.
