Senior QE Test Engineer – SDET

Overview

In this role, you will implement and run automated data tests within an enterprise data platform to assure quality across ingestion, processing, and consumption layers during a data migration transformation. You’ll work in an agile Data Factory setup, collaborating with multi-vendor teams to validate data pipelines, transformations, and analytics outputs. You’ll build reusable test automation for data quality, reconciliation, and schema checks, supporting a data lakehouse migration on AWS. This is a hands-on position with a focus on delivering reliable, auditable data results.

Pay / Benefits

• tailored benefits
• flexible work options
• learning and development opportunities

Responsibilities

• Design, develop and execute automated data tests aligned with functional and non-functional requirements
• Validate data quality across ingestion, processing and consumption layers
• Implement source-to-target reconciliation, schema validation and transformation testing in ETL/ELT environments
• Translate data mappings and business rules into automated test scenarios
• Develop reusable test scripts using approved automation frameworks
• Test data migrations from legacy Data Warehouse to new AWS Data Lakehouse architecture
• Execute automated test suites in sprint-based delivery and regression cycles
• Document test cases, results, and quality metrics; support audit and governance requirements
• Collaborate with Data Engineers, Platform Engineers, Product Owners and external partners to align testing solutions

Key requirements

• Experience in data engineering testing
• Strong knowledge of AWS data pipelines, Data Warehouse and Data Lakehouse concepts
• Ability to build automation frameworks from scratch using OOP and scripting languages
• Experience with Python/PySpark, SQL and YAML configurations
• Experience with CI/CD (GitHub Actions or similar)
• Knowledge of AI-driven testing solutions and automation accelerators
• Experience with test tools (JIRA, XRAY, ADO) for defect management and reporting

• Strong problem-solving and analytical thinking
• Cross-functional collaboration in multi-vendor environments
• Clear evidence-based defect reporting and communication
• Python/PySpark
• SQL
• YAML configurations

…