Automation Test Engineer – Data & Platform Engineering

Location: Madrid or Barcelona, Spain

Working Model: Hybrid – 2–3 days per week from the Madrid or Barcelona office

Contract: Long-term

Role Overview

We are looking for a Senior Automation Test Engineer with strong experience in data platform testing, Python automation, SQL, ETL/ELT, streaming and CI/CD.

This is a hands-on engineering role focused on quality engineering rather than test management. You will design scalable automation frameworks, establish quality standards, validate data pipelines and proactively identify system failures before they reach production.

The idóneo candidate combines strong Python framework development with expertise in data validation, data quality, cloud platforms and distributed systems, while actively using AI-assisted ways of working to accelerate automation and engineering productivity.

Key Responsibilities

- Design and maintain scalable, modular and metadata-driven test automation frameworks.
- Build reusable frameworks for data pipelines, APIs, backend services and end-to-end data products.
- Develop data validation capabilities covering:
- SQL assertions and reconciliation
- Schema validation and evolution
- Data contracts
- Lineage and freshness
- Completeness, accuracy and uniqueness
- Test ETL/ELT and streaming pipelines at scale.
- Validate Kafka/Kinesis event processing, delivery semantics, retries, DLQs and backpressure.
- Proactively test failure scenarios including schema drift, duplicate/missing events, late-arriving data and high-volume workloads.




- Integrate data quality tools such as Great Expectations, Soda or equivalent.
- Embed automated testing and quality gates into CI/CD pipelines using GitHub Actions, Jenkins or similar.
- Develop synthetic, masked and deterministic test data strategies.
- Perform performance, stress, concurrency and data-skew testing.
- Automate security and compliance validations, including PII/PHI exposure, access control and data retention.
- Work closely with data engineers, platform teams and architects to improve system reliability.
- Analyse production incidents and drive permanent root-cause fixes.
- Reduce flaky tests, false positives, execution time and unnecessary compute.
- Leverage AI-assisted engineering and automation practices wherever possible.

Mandatory Skills
- experience in Automation / Quality Engineering.
- Strong Python development with experience building test frameworks, libraries and CLI tools.
- Advanced SQL for data validation and reconciliation.
- Hands-on experience with DBT, Airflow and Snowflake or equivalent data platforms.
- Strong understanding of ETL/ELT and data modelling.
- Experience with Great Expectations, Soda or similar data quality tools.
- Experience testing Kafka, Kinesis or similar streaming platforms.
- Understanding of event-processing and delivery semantics.
- Strong Git and CI/CD experience.
- Experience with Jenkins, GitHub Actions or equivalent.
- AWS experience across data and compute services.
- Knowledge of Terraform, CloudFormation or similar IaC tools.
- Mandatory: AI ways of working / AI-assisted engineering experience.

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