QA Data Automation Engineer (Porto)

act digital; Porto; On-site; EUR 70,000 - 110,000; Full time

Job summary

act digital is seeking a Senior QA Automation Engineer to join the Data Systems team. The role focuses on QA across data-intensive applications during a major Data Warehouse migration, with hands-on SQL and Python for data validation and testing.

You will design automated testing solutions, enhance testing practices, and collaborate with international teams across Europe. The successful candidate will support data distribution applications and cloud-based data processing on AWS, PySpark, EMR,

Qualifications

• 7+ years of professional QA experience.
• Strong experience in Test Automation.
• Experience with AWS environments.
• Good hands‑on knowledge of SQL for data validation and investigation.
• Knowledge of Python for testing, automation, or data processing.
• Experience creating QA dashboards, reports, and technical documentation.
• Ability to work across multiple applications and priorities simultaneously.

Responsibilities

• Own quality assurance across multiple data-intensive applications.
• Design and maintain automated testing solutions.
• Validate ETL pipelines, transformations, and data quality.
• Ensure quality throughout the Data Warehouse migration.
• Use SQL and Python to investigate and validate complex data scenarios.
• Drive improvements in QA automation and testing practices.
• Collaborate with international engineering teams across Europe.

Skills

QA Automation

AWS

SQL

Python

Jira

Confluence

Data Testing

Data Warehousing

Tools

Jira

Confluence

Job description

About the Role

We are looking for a Senior QA Automation Engineer to join the Data Systems team of a major European stock exchange operator.

You will join the Distribution Apps team, responsible for applications that distribute market data to clients, billing platforms, and regulatory systems.

The team is currently undergoing a major multi-year Data Warehouse transformation, modernizing its technology stack:

• Amazon Redshift → Apache Iceberg tables

This transformation is designed to improve platform scalability, simplify the data ecosystem, and reduce operational costs. As a Senior QA Automation Engineer, you will play a critical role in ensuring data quality and application reliability throughout this journey.

As a Senior QA Automation Engineer, you will:

• Own quality assurance across multiple data-intensive applications.
• Design and maintain automated testing solutions.
• Validate ETL pipelines, transformations, and data quality.
• Ensure quality throughout the Data Warehouse migration.
• Use SQL and Python to investigate and validate complex data scenarios.
• Drive improvements in QA automation and testing practices.
• Explore practical applications of AI to improve testing efficiency.
• Collaborate with international engineering teams across Europe.

QA Automation

• Design, develop, execute, and maintain automated test scenarios.
• Work with the team's internally developed automation frameworks.
• Increase automated testing coverage across critical applications.
• Create reusable and maintainable testing components.
• Analyze automated test results and investigate failures.
• Improve regression testing and release confidence.
• Contribute to the evolution of the team's overall automation strategy.

Data & ETL Testing

• Validate complex data processing and ETL workflows.
• Use SQL and Python to test data transformations and business rules.
• Perform reconciliation between source and target datasets.
• Validate:
• Data integrity
• Investigate discrepancies across different stages of data pipelines.

Data Warehouse Migration

• Ensure quality throughout the migration to the new Data Platform.
• Validate the transition from existing technologies to:
• PySpark
• Amazon EMR
• Compare legacy and target platform results.
• Identify potential functional or data-quality regressions.
• Support migration testing and production readiness.
• Work closely with Data Engineers and Developers to resolve migration-related quality issues.

Multi-Application Quality Assurance

• Support approximately 5 to 6 data applications, moving between them according to priorities.
• Gain functional understanding of applications such as:
• Datashop
• Data Transformations
• EFTS
• Adapt testing strategies to different products and use cases.
• Quickly understand new functional contexts.
• Balance competing priorities across applications without compromising testing quality.
• Test data applications and workloads running primarily on AWS.
• Validate cloud-based data processing workflows.
• Investigate issues across applications, pipelines, and cloud services.
• Contribute to testing strategies suitable for scalable and distributed data architectures.
• Support the validation of platform changes throughout the migration.
• Identify, document, and follow defects through to resolution.
• Work closely with developers to analyze root causes.
• Retest fixes and validate successful resolution.
• Investigate complex application and data-related issues.
• Ensure clear traceability between requirements, tests, defects, and resolutions.

Jira, Reporting & Documentation

• Create and maintain QA dashboards in Jira.
• Track testing progress, defects, priorities, and relevant quality indicators.
• Produce clear testing evidence and status information.
• Communicate quality risks and testing progress to stakeholders.

AI-Assisted Quality Engineering

• Explore opportunities to apply Artificial Intelligence to QA and test automation.
• Identify practical use cases for accelerating:
• Test case creation
• Defect analysis
• Documentation
• Evaluate AI-generated outputs critically before incorporating them into testing processes.
• Share useful AI-driven QA practices with the wider team.
• Challenge existing QA practices and propose improvements.
• Identify opportunities to increase automation and reduce repetitive manual activities.
• Improve test reliability, maintainability, and execution efficiency.
• Share knowledge and quality engineering best practices across the team.
• Help strengthen the overall QA maturity of the Data Systems environment.

What We're Looking For

Mandatory Requirements

• 7+ years of professional QA experience.
• Strong experience in Test Automation.
• Experience working with AWS environments.
• Good hands‑on knowledge of SQL for data validation and investigation.
• Knowledge of Python for testing, automation, or data processing.
• Strong experience with:
• Jira
• Confluence
• Experience creating QA dashboards, reports, and technical documentation.
• Ability to work across multiple applications and priorities simultaneously.
• Strong analytical and troubleshooting capabilities.
• Ability to quickly understand unfamiliar functional domains.
• Interest and openness to incorporating AI into QA activities.
• Professional English for collaboration with international teams.

Quality Engineering

• Test Automation
• Functional Testing
• Regression Testing
• Integration Testing
• Data Testing

Data Testing

• SQL
• Python
• ETL TestingData Validation
• Data Quality
• Data Warehouse Testing
• AWS
• Amazon EMR
• PySpark
• Amazon Redshift
• Distributed Data Processing
• Jira
• Confluence
• Test Documentation

AI & Automation

• AI-Assisted Testing
• Test Automation Improvement
• AI for Test Generation

Nice to Have

• Previous experience in Data Engineering or Data Platform projects.
• Experience testing:
• Data Warehouses
• Data Lakes
• Hands‑on knowledge of PySpark.
• Experience with Amazon EMR.
• Knowledge of Apache Iceberg.
• Previous exposure to Amazon Redshift.
• Practical AI use cases applied to QA or automation.
• Experience in Financial Services, Stock Exchanges, Capital Markets, or Market Data.
• Experience working with complex, high‑volume data environments.

Analytical Thinking

• Strong analytical and investigative mindset.
• Ability to trace data issues across complex processing flows.
• Attention to accuracy, consistency, and detail.
• Ability to move between several applications according to priorities.
• Flexible approach within a large transformation program.

Ownership

• Ability to take responsibility for quality within assigned applications.
• Proactive identification of risks and improvement opportunities.
• Strong commitment to reliable and production‑ready solutions.
• Comfortable working with Data Engineers, Developers, Business Analysts, and international teams.
• Ability to communicate defects and quality risks clearly.
• Strong knowledge‑sharing and collaborative mindset.
• Willingness to challenge existing processes constructively.
• Interest in automation, AI, and emerging QA practices.
• Focus on continuously improving efficiency and testing quality.
• Work on systems supporting market data, billing, and regulatory processes.
• Combine QA Automation, Data Engineering concepts, AWS, SQL, and Python.
• Gain exposure to a modern data stack involving PySpark, EMR, and Iceberg.
• Influence QA practices across several applications.
• Collaborate with teams across multiple European locations.
• Explore concrete applications of AI within Quality Engineering.
• Contribute directly to a transformation focused on scalability and operational efficiency.