Quality Assurance Engineer (Data Platforms)

TL;DR

Quality Assurance Engineer (Data Platforms): Design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures with an accent on data validation, integrity, and platform reliability. Focus on leveraging AI-driven tools for test case generation, anomaly detection, and enhancing QA processes in cloud-based enterprise data platforms.

Location: U.S. Citizenship required; ability to obtain and maintain a federal clearance.

Company

hirify.global supports enterprise data platforms for federal clients.

What you will do

• Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
• Build reusable testing utilities for data validation, regression testing, and pipeline certification; integrate into CI/CD pipelines.
• Validate ETL/ELT pipelines, complex datasets like XBRL, and Apache Iceberg architectures including schema evolution, time travel, and partitioning.
• Test materialized views for performance, consistency, data freshness, and alignment with source data.
• Apply AI/ML tools for intelligent test generation, defect prediction, anomaly detection, and validating data for AI use cases.
• Collaborate with data engineers, architects, and analysts in Agile teams to define test strategies and document quality metrics.

Requirements

• U.S. Citizenship required; ability to obtain and maintain a federal clearance.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
• 5+ years of experience in QA engineering, data testing, or software development.
• Strong programming skills in Python and advanced proficiency in SQL.
• Experience building automated test frameworks for data platforms and ETL pipelines.
• Hands-on experience with AWS data services (S3, Glue, Redshift, Lambda), Apache Iceberg, materialized views, XBRL datasets, CI/CD tools, and AI-assisted workflows.
• Understanding of data modeling, metadata, governance, and data quality for AI/ML use cases.

Nice to have

• Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve.
• Familiarity with data catalog tools (Collibra, Alation), Apache Spark, data quality tools, and data maturity frameworks.
• Experience testing large-scale cloud data platforms and lakehouse architectures for AI/ML solutions.