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Data & Analytics QA Engineer

Everpure, Inc.Santa Clara, California🇺🇸United StatesPosted Sep 28, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Santa Clara, California, United States
GCPSQLAWSETLAzureData PipelinePython

Job Description

Everpure, Inc. seeks a Quality Systems Engineer to own and evolve quality frameworks for our cloud data and analytics solutions. You'll design and maintain quality management systems, define test strategies, and automate data validation across pipelines and platforms. Partnering with data engineers and consultants, you'll ensure data integrity, performance, and reliability for client-facing solutions. You'll track quality metrics, investigate issues, and drive continuous improvement. At Everpure, you'll work in a collaborative, learning-focused culture, experimenting with modern tools while tackling complex data challenges that deliver real business impact.

Responsibilities

  • Design, implement, and maintain quality management systems for Everpure's data and analytics solutions
  • Develop and refine QA processes for data pipelines, cloud data platforms, and analytics products
  • Create and execute test plans for data integrity, performance, and reliability across environments
  • Collaborate with data engineers and consultants to embed quality standards into solution design
  • Define and track quality metrics, SLAs, and KPIs for data products and platforms
  • Conduct root-cause analysis and drive corrective and preventive actions for quality issues
  • Automate data validation, regression tests, and monitoring using modern QA tooling
  • Ensure compliance with security, governance, and documentation standards
  • Partner with clients to understand requirements and translate them into quality criteria
  • Contribute to continuous improvement of engineering and delivery practices

Required Skills

  • Quality management systems (QMS) design and implementation
  • Data pipeline testing and validation (ETL/ELT)
  • SQL for data profiling and validation
  • Cloud data platforms (AWS, Azure, or GCP)
  • Test automation frameworks and scripting (e.g., Python)
  • Data modeling and warehousing concepts
  • Monitoring, logging, and quality metrics/KPIs
  • Root cause analysis and corrective/preventive actions
  • Version control and CI/CD pipelines
  • Documentation and process definition for QA and quality systems

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