Middle/Senior Test Engineer (Data Experience) IRC289738

Description

Our client is a luxury skincare and beauty brand. Based in San Francisco, they sell high-end skincare products to a global market.
The brand’s primary IT “product” is its e-commerce website, which functions as a digital platform to sell products, educate customers, and personalize user experiences.

Runs on Salesforce Commerce Cloud (formerly Demandware) — an enterprise e-commerce platform that supports online shopping, order processing, customer accounts, and product catalogs.
Hosted on cloud infrastructure (e.g., AWS, Cloudflare) for reliable performance and security
Uses HTTPS/SSL encryption to secure data transfers.
Integrated marketing and analytics technologies such as Klaviyo (email & SMS automation), Google Tag Manager, and personalization tools to track behavior, optimize campaigns, and increase conversions
It’s both a shopping platform and a digital touchpoint for customers worldwide.

Requirements

Technical Skills & Qualifications:

• SQL Expertise: Proficient in writing complex SQL queries for data validation, transformation testing, and troubleshooting.
• ETL Testing: Strong experience with ETL processes and tools (e.g., Talend, Apache Nifi, Informatica) for testing data pipelines and workflows.
• Data Warehousing: Understanding of data warehouse architecture (e.g., Snowflake, Redshift, BigQuery) and the ability to validate data storage and retrieval.
• Data Governance: Knowledge of data governance principles, data quality metrics, and validation techniques.
• Scripting Languages: Proficiency in scripting languages (e.g., Python, Bash) for data validation.
• Version Control: Familiar with version control systems like Git for managing testing scripts and documentation.
• Defect Tracking Tools: Expertise in tools like Jira or Azure DevOps for issue tracking and test management.
• Performance Testing: Ability to conduct performance and load testing of data pipelines using appropriate tools and techniques.

Desired Experience:

• Experience: 3+ years in data engineering or related roles with a focus on data migration, cloud platforms, and large-scale data processing.
• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
• Certifications: QA certifications (e.g., ISTQB) are a plus, along with certifications in data engineering tools or cloud platforms (e.g., AWS, GCP, Azure).
• Data Engineering Knowledge: Strong understanding of data engineering concepts and familiarity with data models, schemas, and relational databases.
• Collaboration: Proven experience working closely with data engineers, developers, and project stakeholders to ensure smooth communication and issue resolution

Job responsibilities

• Test Planning and Strategy Development: Design comprehensive test strategies, plans, and scenarios to validate the accuracy, completeness, and performance of data pipelines, ensuring alignment with project goals and data governance policies.
• Data Quality Assurance: Perform detailed data validation checks to ensure data accuracy, completeness, and consistency across various data stages using SQL queries and other validation techniques.
• ETL Process Testing: Test and validate Extract, Transform, Load (ETL) processes to ensure proper data transformation and integration from source systems to data warehouses or databases.
• Automation Framework Management: Develop, maintain, and execute automated test scripts for continuous testing of data flows, minimizing manual effort and ensuring consistent quality across releases.
• Performance and Load Testing: Conduct performance testing of data pipelines and storage systems to ensure efficient processing and data flow for large datasets, identifying and resolving bottlenecks.
• Defect Management: Identify and document defects, inconsistencies, and issues related to data quality, and track their resolution using issue tracking tools (e.g., Jira), ensuring proper communication with development teams.
• Cross-Team Collaboration: Collaborate with data engineers, developers, business analysts, and other project stakeholders to define testing requirements, resolve issues, and ensure that data processes meet business and technical requirements.
• Test Documentation and Reporting: Create and maintain clear, detailed test plans, test cases, and defect reports, and provide regular updates on testing progress and results to stakeholders.
• Compliance and Data Governance: Ensure that all data testing processes adhere to organizational data governance standards, policies, and best practices.
• Continuous Improvement: Regularly review testing processes, tools, and methodologies to identify areas for improvement and implement best practices to enhance testing efficiency and data quality.