Cloud Data Testing Engineer (Mid shift/Hybrid)

Job Overview:

The Cloud Data Testing Engineer will own quality assurance across our data pipeline environment. A qualified applicant will trace data as it moves through the pipeline to identify where and why failures occur. They will work closely with data engineers, developers, and project stakeholders to ensure data accuracy, consistency, and reliability across ingestion, transformation, and delivery. This is a strong fit for someone who is comfortable reading and debugging pipeline code, writing SQL to independently verify data at rest, and building test strategies around data movement rather than just UI behavior.

Key Responsibilities:

● Trace data lineage across the pipeline to identify potential points of failure, transformation errors, or data loss

● Design, develop, and execute test plans and test cases covering ETL/ELT processes, data

pipelines, and data integration points

● Write and optimize SQL queries to independently validate data accuracy, consistency, and

completeness at each stage of the pipeline

● Collaborate with data engineers and developers to define data quality checks and validation rules throughout the pipeline lifecycle

● Document, track, and communicate defects and data quality issues, working cross-functionally to drive resolution

● Support automated testing and validation frameworks for ongoing data quality monitoring

● Provide QA input during design and code review

Core Expertise:

● Experience: At least 5 years in a data engineering role working on validation of data integration, processing and transformation pipelines.

● Test Lifecycle Management: Skilled in end-to-end data test planning, scenario design, automated script writing, execution, and quality reporting.

● Pipeline Orchestration Testing: Experience validating the orchestration and failure boundaries of data flows and pipelines.

● Demonstrated proficiency with SQL for deep-dive data reconciliation, data profiling, and backend verification

● Combines QA practices (planning, automation, and reporting) with data engineering to validate data pipeline integrity, quality, and performance for production readiness.

● Demonstrated client-facing experience, multi-cultural team experience, technical leadership, team leadership.

● Nice to have:

o Working knowledge of AWS services: Lambda, RDS, S3

o Strong Python skills for test frameworks, API integration, and data validation,

emphasizing code quality and TDD.

o Practical knowledge of big data storage formats (Apache Parquet, ORC), including

schema validation, file size optimization testing, and identifying data corruption pitfalls.

Abilities

● Ability to work both autonomously and as part of a team

● Attention to quality and details

● Good critical and analytical thinking

● Good time management and organizational skills

● Customer / end-client orientation

● Excellent written and spoken communication skills