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

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