Sr. Test Data Automation Engineer

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)
No Travel Required

Skills

Automated Testing
Amazon S3
Continuous Delivery
Continuous Integration
Data Architecture
Data Engineering
Data Modeling
Data Quality
Database
Databricks
Extract
Transform
Load
Jupyter
PySpark
Python
Real-time
SAFE
Sprint
Stacks Blockchain
Testing
UPS
Unit Testing
Agile
Data Mesh
Data Testing

Job Details

Job Title: Sr. Test Data Automation Engineer (2190)
Location: Hybrid EST/CST Time Zones (3 Days Onsite/Week)
Duration: 12-Month Contract

Job Overview:

The Federal Reserve Bank is seeking a Senior Test Data Automation Engineer to join its Agile Data Engineering team. The ideal candidate will have hands-on experience in designing, building, and testing automated data pipelines and ensuring data quality and reliability using modern data stack and test frameworks. This position focuses on automation-first strategies for data ingestion, transformation, and validation at scale.

Key Responsibilities:

  • Lead the design and development of automated test frameworks and reusable test components for data pipelines.
  • Actively participate in Agile ceremonies (e.g., Sprint Planning, Stand-ups, Retrospectives) as part of the CDP Agile squad.
  • Develop and implement robust data pipeline testing strategies and data quality monitoring using tools like Great Expectations or Deequ.
  • Build and maintain scalable data pipelines using PySpark, Databricks, and Airflow within Lakehouse and Data Mesh architecture

Required Qualifications:

  • 4+ years experience in data architecture, data modeling, and building data pipelines/distributed systems at scale.
  • 3+ years hands-on experience with Python and PySpark.
  • 2+ years experience working with Databricks, Collibra, and Starburst.
  • 2+ years experience in modern data stacks: Object stores (e.g., S3), Airflow, real-time databases, and Lakehouse architectures.
  • Solid understanding of Data Mesh frameworks and CI/CD testing strategies for data ingestion.
  • Proven experience developing and unit testing in Jupyter notebooks.
  • Hands-on experience with data quality frameworks like Great Expectations or Deequ.
  • Familiar with Agile and SAFe methodologies.
  • Ability to document and enforce data testing standards and policies.
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