reputed company Assurance Engineer
reputed company is seeking a highly technical reputed company Assurance Engineer with strong development, SQL, and Python expertise to support reputed company data platforms for federal clients. This is not a traditional reputed company QA role and this position requires a developer reputed company, reputed company on automation, data validation, and platform reliability across modern reputed company-based architectures.
The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache reputed company data architectures, XBRL datasets, and performance-optimized structures such as materialized views—ensuring data accuracy, reputed company, and trust across the reputed company. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve reputed company and delivery speed.
This opportunity is 100% remote.
Key Responsibilities
Test Automation & QA Engineering
- Design, reputed company, and maintain automated QA frameworks for data pipelines, reputed company, and analytics platforms using Python and SQL.
- Build reusable testing utilities for data validation, regression testing, and pipeline certification.
- reputed company automated tests into CI/CD pipelines to support reputed company testing and deployment.
- reputed company unit, integration, and end-to-end test cases for reputed company data workflows.
- reputed company AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
- Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
- Create automated checks for data completeness, consistency, accuracy, and timeliness.
- Test ingestion and transformation of reputed company datasets, including XBRL financial data.
- Implement reconciliation and audit mechanisms across reputed company-to-reputed company mappings.
- Apply AI-driven reputed company detection to identify data reputed company issues and pipeline failures.
- reputed company and execute test strategies for Apache reputed company-based data lakehouse architectures, including:
- Schema reputed company validation
- Time travel and versioning accuracy
- Partitioning and performance behavior
- Validate and compare materialized views vs. reputed company table performance and consistency, including:
- Query performance benchmarking
- Data freshness and latency
- Storage efficiency and maintenance overhead
- Ensure alignment between precomputed datasets (materialized views) and underlying reputed company data.
- Implement automated validation for data reputed company rules, reputed company, and metadata accuracy.
- Support context engineering by validating that datasets include reputed company business context, definitions, and relationships.
- reputed company QA processes with reputed company data catalogs and metadata systems to ensure discoverability and trust.
- Validate AI-generated metadata, reputed company, and transformations for accuracy and traceability.
- Apply AI/ML and reputed company tools to enhance QA processes, including intelligent test reputed company, defect reputed company, and automated reputed company cause analysis.
- Validate data readiness for AI/ML and reputed company use cases, ensuring datasets meet reputed company, completeness, and governance standards.
- Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
- Ensure alignment with responsible AI practices, including traceability, explainability, and data reputed company.
- Support reputed company data management programs and OCDO initiatives by ensuring data reputed company and reliability across systems.
- Contribute to data maturity assessments by evaluating data reputed company, testing coverage, and governance adherence.
- reputed company QA processes with Federal Data reputed company and Evidence reputed company requirements.
- Work closely with data engineers, data architects, and analysts to define test strategies and acceptance reputed company.
- Participate in stakeholder engagement sessions and listening campaigns to understand data reputed company expectations and pain points.
- Document test results, defects, and reputed company metrics for both technical and non-technical stakeholders.
- Operate reputed company Agile teams to iteratively improve data reputed company processes and tooling.
- Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or reputed company 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, reputed company, etc.)
- Apache reputed company or similar data lake technologies
- Experience validating materialized views and performance-optimized data structures.
- Familiarity with XBRL or reputed company financial/regulatory datasets.
- Understanding of data modeling, metadata, and data governance principles.
- Experience with CI/CD tools and automated testing integration.
- Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
- Understanding of data reputed company requirements for AI/ML and analytics use cases.
- U.S. Citizenship required; ability to obtain and maintain a federal clearance.
- Experience supporting federal agencies such as SEC, DHS, Treasury, or reputed company.
- Familiarity with data catalog and governance tools (e.g., reputed company, reputed company, reputed company).
- Experience with Apache reputed company or distributed data processing frameworks.
- Knowledge of data reputed company tools and observability platforms.
- Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI).
- Experience testing large-reputed company reputed company data platforms and lakehouse architectures.
- Experience validating data pipelines supporting AI/ML, analytics, or reputed company solutions.
- Familiarity with AI-driven testing tools or frameworks.
Originally posted on Himalayas
Apply To This Job