Automation Test Engineer

🚀 Automation Test Engineer – Data & Platform Engineering

Location: Madrid or Barcelona, Spain

Hybrid: 2–3 days per week from the Madrid/Barcelona office

Contract: Long-term

🤖 AI Ways of Working: Mandatory

About the Role

We’re looking for an experienced Automation Test Engineer to own Quality Engineering across data and platform engineering.

This is not a Test Lead or Test Management role. You’ll be hands-on, building scalable automation frameworks, designing data validation strategies, challenging weak implementations, and creating quality gates that prevent issues from reaching production.

You should be highly comfortable with Python, SQL, data testing, automation frameworks, streaming systems and cloud technologies, while actively using AI-assisted ways of working to accelerate automation and engineering productivity.

What You’ll Be Doing

• Design and evolve scalable, modular, metadata-driven test automation frameworks
• Build automation for batch and streaming data pipelines, APIs and backend services
• Develop reusable data testing capabilities for:
• SQL assertions and reconciliation
• Schema validation and evolution
• Data contracts
• Lineage and freshness
• YAML/JSON-based test configurations
• Proactively test failure scenarios including:
• Schema drift
• Duplicate, missing and out-of-order events
• Late-arriving data
• Retries, DLQs and backpressure
• High-volume and concurrency issues
• Validate ETL/ELT pipelines using row, aggregate and hash-based reconciliation
• Test incremental loads, backfills and event-processing semantics
• Integrate and extend Great Expectations, Soda or similar data-quality tools
• Embed automated quality gates into CI/CD and DataOps pipelines
• Work with GitHub Actions, Jenkins or equivalent
• Develop strategies for synthetic, masked and deterministic test data
• Perform performance, stress and reliability testing across data platforms
• Automate security and compliance validations around PII/PHI, encryption, access control and data retention
• Work closely with Data Engineers, Platform Engineers and Architects to embed quality early
• Analyse production failures and drive root-cause fixes
• Reduce flaky tests, false positives and unnecessary compute
• Use AI tools and AI-assisted engineering practices wherever possible to accelerate automation

🛠️ Mandatory Skills

Programming & Automation

• Strong Python
• Experience building automation/test frameworks, libraries and CLI tools
• Advanced SQL
• Strong test automation and framework engineering experience

Data Engineering

• DBT
• Airflow
• Snowflake or similar data platforms
• Strong ETL/ELT and data modelling knowledge

Data Quality

• Great Expectations, Soda or similar
• Data validation and reconciliation
• Schema validation and data contracts
• Data lineage and freshness

Streaming

• Kafka, Kinesis or similar
• Event processing and delivery semantics
• Experience testing distributed/streaming systems

CI/CD & DevOps

• Strong Git
• Jenkins, GitHub Actions or equivalent
• CI/CD quality gates

Cloud & Infrastructure

• AWS – data and compute services
• Terraform, CloudFormation or similar IaC

AI Ways of Working

• Practical experience using AI/GenAI tools to improve software testing, automation or engineering productivity – Mandatory

⭐ Nice to Have

• API contract testing / PACT
• UI automation
• Data Mesh / Data Product architectures
• Prometheus / Grafana
• Experience in regulated industries such as Healthcare, Life Sciences or Finance
• Security and compliance testing

Experience

• 9+ years in Automation / Quality Engineering
• 5+ years building testing frameworks for data platforms at scale
• Strong hands-on engineering background rather than test management
• Proven experience improving reliability and reducing production incidents