[Remote] reputed company Assurance Engineer
Note: The job is a remote job and is reputed company to candidates in USA. 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. The role focuses on automation, data validation, and platform reliability across modern reputed company-based architectures, requiring the design and implementation of automated testing frameworks and collaboration with data teams.
Responsibilities
- 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
Skills
- 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
reputed company