Data Engineer AI & Data Platforms

Why Keyrus, Why Now!

Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.

For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence.

AI does not replace humans. It repositions us to a place no system can follow: understanding, deciding, designing, and creating.

At Keyrus, you will not just develop skills — you will develop judgment. Your expertise sharpens with every system you architect, every client challenge you solve, and every deployment that compounds on the last.

Over time, you grow into one of the rarest professionals of the intelligence era: someone who bridges data, AI, and human decision-making at scale, across industries and geographies. This is not a role you fill. It is a discipline you master and a story you help write to become a Keyrus Architect of Intelligence.

Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

Role Details

Job location: Madrid, Spain (Hybrid model when needed)
Contract type: Employee
Target start date: January 2027
⏰ Working hours: Full-time
Compensation: €40k–€54k per year

What You'll Architect

As a Data Engineer – AI & Data Platforms, you will help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision-making.

This role is both technical and consultative — perfect for someone who can combine hands-on data engineering, solution design, and close collaboration with client and delivery teams. We’re looking for professionals who think like architects and act like builders.

This is fundamentally a Data Engineering role, with an added focus on preparing enterprise data for modern AI. Intelligent systems are only as dependable as the data beneath them: you will turn structured and unstructured source data into trusted, reusable data products that serve analytics and applications as well as RAG, retrieval, evaluation and agent-enabled systems.

• Design, build, test and operate production data pipelines and data products, moving data reliably from enterprise source systems into reusable consumption layers.

• Develop and maintain data models and transformation logic that preserve clear definitions and business context across analytical, operational and AI workloads.

• Implement data quality, lineage, metadata and observability controls, so critical data is trustworthy, understandable and monitored in production.

• Prepare structured and unstructured data for intelligent systems, including retrieval, grounding, RAG indexes, semantic context and evaluation datasets.

• Engineer reliable ingestion and processing patterns across APIs, databases, files, documents, events and other enterprise sources.

• Operate and improve data workloads in a major cloud data ecosystem, balancing reliability, scalability, security, performance and cost.

• Collaborate with AI Engineers and Software Engineers on stable data interfaces, without taking ownership of model behaviour, agents or the surrounding application.

• Document and reuse what works, including data contracts, transformations, controls, dependencies and operational knowledge that strengthen future Keyrus engagements.

Who You Are

• You are a hands-on Data Engineer who knows that moving data is not enough: it must remain trusted, understandable and usable once a system is in production.

• You think from source to consumption and can turn fragmented enterprise data into reliable data products.

• You understand that analytics, applications, RAG systems, evaluations and agents consume data differently, and you design the foundation accordingly.

• You combine strong engineering fundamentals with practical judgement.

• You take ownership of what you build and follow it through to production.

• You collaborate easily with AI, software, analytics, architecture and governance teams in international environments.

️ What You Bring

Qualifications & Experience

• Degree in Computer Science, Engineering, Information Systems or a related field, or equivalent professional experience.

• Strong hands-on experience designing, building and operating production data pipelines and data products.

• Deep practical expertise in at least one major cloud data ecosystem, with the ability to apply the same principles across comparable technologies.

• Experience working with AI, software, analytics, architecture or governance teams that consume or define requirements for your data products.

• Availability for monthly travel to Madrid or Lisbon.

• Professional fluency in Spanish and English, written and spoken.

Technical & Professional Skills

• Strong SQL and data-modelling fundamentals, with the ability to design clear, maintainable transformation and consumption layers.

• Production engineering capability in Python or another language commonly used for data engineering.

• Experience implementing data quality, testing, monitoring, lineage or observability within production data workflows.

• Understanding of production concerns including reliability, scalability, security, access, maintainability and performance.

• Experience preparing structured and unstructured data for AI / GenAI use cases, and a practical understanding of how RAG, embeddings, semantic context and evaluation datasets depend on enterprise data.

• Experience with vector or hybrid search infrastructure, including feeding and maintaining RAG indexes.

• Experience with distributed processing, orchestration and modern transformation approaches, covering batch ingestion plus APIs, change data capture, event-driven processing or streaming.

• Working knowledge of CI/CD, automated testing, version control and repeatable deployment for data solutions.

• Rapid prototyping, from mock-up to working proof of concept in days (e.g. Streamlit).

• Depth in at least one environment such as Databricks, Snowflake, Microsoft Fabric, BigQuery, Redshift, Spark, dbt or Airflow. These are examples, not a checklist.

Nice to Have

• Experience with semantic layers, knowledge graphs, ontologies or enterprise knowledge architectures.

• Experience building data foundations for agentic systems or complex GenAI applications.

• Experience with real-time or high-volume streaming architectures.

• Experience with ML data pipelines, feature stores or MLOps integration.

• BI dashboards on top of the data products when needed.

• Experience in regulated, high-security or data-sovereignty-sensitive environments.

• Consulting or client-facing experience, and exposure to international projects or multicultural teams.

⭐ What Makes You Successful

• You think in terms of trusted data products, not pipelines as an end in themselves.

• You combine strong engineering fundamentals with production discipline.

• You identify data-quality and reliability risks before they become downstream AI or application failures.

• You know when a requirement belongs to Data Engineering and when it should move to AI, Software, Architecture or Governance.

• You make pragmatic decisions about performance, scalability, maintainability and cost.

• You document what you build and turn successful data patterns into reusable knowledge across engagements.

What We Offer at Keyrus Spain

• Competitive salary, paid in 14 instalments (option for 12 from day one).

• Annual performance bonus.

• Meal allowance (Restaurant Tickets): €1,800/year net, distributed over 11 monthly reloads

• Flexible retribution plan: transport & daycare tickets

• Private medical insurance with Sanitas (no copays, dental included)

• 22 days of annual leave, increasing every 3 years (up to 26 days)

• 14 public holidays (Madrid calendar)

• December 24th and 31st are considered company holidays when they fall on working days

• Continuous learning via KLX – Keyrus Learning Experience

• A collaborative, international, and human-centred work environment

How Our Salary Ranges Work

At Keyrus, salary ranges reflect different levels of mastery and impact within the same role — not different job titles.

• Bottom of the range
  You meet the core requirements and will need ramp-up time and support.

• Middle of the range
  You are fully autonomous from Day 1 and deliver consistently.

• Top of the range
  You are a reference for the role, mentor others, and raise the bar for the team.

Final offers are based on experience, autonomy, scope, and market context, and are discussed transparently during the process.

Responsible AI & Recruitment

At Keyrus, all stages of our recruitment process are conducted and evaluated by human recruiters and interviewers.
To support accuracy and efficiency, AI may occasionally be used internally by our team exclusively for note-taking purposes during interviews. AI is never used to make decisions.

To ensure fairness, authenticity, and the protection of confidential and proprietary information, the use of AI tools by candidates during the recruitment process is strictly prohibited.

Our commitment to responsible AI practices ensures that hiring decisions are based solely on each candidate’s own skills, experience, judgment, and expertise.

⚠️ Any use of AI assistance during the interview process may result in immediate disqualification from the recruitment process.

♿ Equal Opportunity Statement

We are committed to building an inclusive workplace and encourage applications from all backgrounds, regardless of race, ethnicity, gender identity, sexual orientation, age, disability, or any other protected characteristic.