> Markdown version of [/jobs/ext/2086872-data-quality-engineer](https://www.wearedevelopers.com/jobs/ext/2086872-data-quality-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Quality Engineer

- **Company:** Proudtobebunge
- **Location:** Sant Just, Spain
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), BigQuery, Cloud Computing, Cloud Storage, Data as a Services, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Data Profiling, Database Queries, Distributed Data Store, Data Flow Control, Information Management, Python (Programming Language), Meta-Data Management, DataOps, Cloudera, Data Processing, Scripting, Google Cloud, Large Language Models, Multi-Agent Systems, Information Technology, Data Analytics
- **Published:** August 16, 2026
- **Apply:** https://es.trabajo.org/oferta-4021-c0c9c4c97b4f063c326c6d834c7f3b5d

## About the Role

strategy in Data Observability and Self-service consumption at scale to cover all data products in the Platform. - Contribute to the design and evolution of the overall GCP-based Data Platform's capabilities related to data quality, observability, and metadata management, specifically for services like BigQuery, Cloud Storage, Dataflow, Dataproc, and Pub/Sub. - Evaluate and recommend new data quality technologies and tools that align with our Data Mesh vision and integrate seamlessly with our existing GCP ecosystem. - Communicate regularly with stakeholders on data quality progress, challenges, and opportunities. Education and Experience: - Bachelor's degree in computer science, Information Management, or a related field. - Minimum of 5 years of experience in Data Quality Engineering, Data Engineering, or a related data role with a strong focus on data reliability. - Proven track record of designing and implementing data quality frameworks at scale within a GCP
environment, ideally applying DataOps practices. - NIce to have: Experience building multi-agent systems or orchestrations - Hands-on experience in building data quality solutions within Data Mesh architecture. - Relevant GCP certifications (e.g., Professional Data Engineer, Cloud Architect, Data Analytics Specialization) are highly preferred. - Deep understanding of Data Platform, Data Products, and Data Mesh principles, with practical experience in implementing distributed data quality. - Strong knowledge with hands-on experience in data quality management processes and techniques, including data profiling, validation, monitoring, and remediation. - Expertise in Google Cloud Platform (GCP) data services is essential, including: - Proficiency in Python for data manipulation, API, scripting, and building data quality tools. - Experience in prompting engineering to define data quality rules using some of LLM in the market - Strong SQL skills and experience

## Description

role about centralizing data quality checks, but about enabling a distributed and automated framework where quality is embedded at the source and throughout the data product lifecycle, with a strong focus on GCP-native quality and GenAI solutions. Main Accountabilitie: - Collaborate with Data Governance and Data Product Owners to define and evolve global data quality standards, policies, and metrics aligned with our Data Mesh principles. - Advise on data contract design to embed quality expectations and validations at the point of data production, leveraging GCP services for enforcement. - Champion a data-as-a-product mindset, ensuring data quality is seen as a core attribute of product excellence across GCP data products. - Design, develop, and maintain self-serve data quality frameworks (DQaaS)., libraries, and tools that empower domain teams to implement, monitor, and enforce quality checks on their own data products within the GCP environment. - Define and implement the


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai)
- [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story)
- [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)