Senior AI Data Engineer, Data Products & RAG Foundations

ES00 Agilent Technologies Spain S.L.

Location: Barcelona
Work arrangement: On-site
Job type: Full-time
Posted Oct 19

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Key skills in this ad

Skills and tools

Data engineering
Warehouse
Snowflake
Data science
Recruitment
Laboratory

Experience

at least 8+ years of relevant experience

Qualifications

Bachelor’s

About this role

Job Description

Agilent helps laboratories around the world advance scientific discovery, diagnostics, and applied market solutions through instruments, software, consumables, services, and deep domain expertise.

About the role:

As a Senior AI Data Engineer, Data Products & RAG Foundations, you will be a data engineering SME within a cross-functional AI pod, working alongside AI engineers, domain experts, business stakeholders, data owners, and platform teams. Your role is to build data products, pipelines, metadata, and retrieval-ready assets that power AI-enabled business and scientific workflows across the enterprise.

Pods do not wait for the enterprise data foundation to be complete; they help build it through execution. Every data product created by the pod is designed for governance, reuse, and long-term value, with the next consumer in mind from day one.

This role goes beyond traditional data engineering. You will work with structured and unstructured data, semantic definitions, quality scoring, lineage, contracts, embeddings, vector search, and retrieval foundations for AI systems. You will also leverage AI-assisted techniques, such as metadata generation, entity resolution, and content classification, to create trusted, AI-ready data products at scale.

You do not need prior experience with Agilent’s internal data architecture. We are looking for a strong data engineer who understands data quality, governance, and AI-ready data foundations and is excited to help shape the future of enterprise AI at Agilent.

What you will do:

Data Products & Governance

Build and maintain AI-ready data products and pipelines for the pod's use case, ensuring appropriate governance, lineage, metadata, access controls, and documentation from the start.

Design data products for reuse, treating every asset as a potential enterprise capability rather than a point integration.

Data Quality and Trust

Establish data quality standards, quality scoring, and model-readiness criteria that support reliable AI behavior and business outcomes.

Ensure quality issues are identified and addressed before they impact downstream AI solutions.

Domain Understanding and Partnership

Partner with data owners, stewards, business stakeholders, and IT teams to establish trusted definitions, authoritative sources, and domain data models.

Ensure AI solutions are grounded in validated business meaning rather than convenience-based access to data.

Retrieval and AI Foundations

Design retrieval foundations that support AI applications, including structured and unstructured grounding, vector search, graph-based approaches, and semantic enrichment where appropriate.

Apply AI-assisted techniques such as metadata generation, entity resolution, and content classification to improve the quality, scalability, and discoverability of data assets.

Engineering Delivery and Reuse

Design and implement scalable ingestion, integration, and storage frameworks across cloud and on-premises environments.

Build reusable data assets, tools, and services that support AI engineers, data scientists, and analytics teams.

Contribute reusable data products, patterns, and documentation back to the broader enterprise ecosystem.

What success looks like in the first year

The pod's use case is running entirely on governed, quality-scored data products, with no undocumented or unsupported data sources.

Multiple data products created by the pod have been adopted, reused, or identified for reuse across additional AI or analytics use cases.

Data quality signals are integrated into AI evaluation and monitoring processes, influencing AI behavior and outcomes.

Data-to-build time has measurably improved through reuse, automation, and process optimization.

Qualifications

Technical Expertise

Strong data engineering experience building AI-ready data products, not just warehouse tables and dashboards.

Hands-on familiarity with platforms such as Microsoft Fabric, Snowflake, vector databases, graph stores, and operating under data contracts, lineage, and certification requirements.

Experience with RAG foundations, including chunking, embedding, hybrid retrieval, and understanding how retrieval quality impacts agent/ AI behavior and outcomes.

Domain and Product Mindset

A disposition to work within a business domain, partnering with data stewards and subject matter experts to understand the meaning behind the data.

An instinct to build for reuse, creating assets intended for second consumers and use cases, not just the first.

Communication and Influence

Excellent communication and the ability to influence technical and non-technical audiences.

Able to build trusted partnerships with domain experts, stewards, business stakeholders and functions such as Legal, Quality, and Security.

Curiosity and Growth Mindset

Curiosity about AI, its opportunities, limitations, staying informed about emerging approaches, while maintaining a healthy skepticism and focus on responsible implementation.

A lifelong learner who continuously adapts skills and ways of working in a rapidly evolving field.

Education and Seniority

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.

Typically, at least 8+ years of relevant experience for entry to this level.

Additional Details

This job has a full time weekly schedule.

Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations

Agilent Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

Travel Required:

10% of the Time

Shift:

Day

Duration:

No End Date

Job Function:

Administration

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As a global leader in laboratory and clinical technologies, we are passionate about bringing great science to life.

Our commitment to quality and innovation supports cutting-edge life science research, patient diagnostics, and ensures the safety of water, food, and pharmaceuticals. As scientists and clinicians pursue small, everyday advances and life-changing discoveries, we provide trusted answers to their most critical questions and challenges.

Leveraging more than 50 years of expertise, we create advanced instruments, software, and consumables supported by teams of highly skilled and knowledgeable people. With 18,000 employees around the world, our global reach and comprehensive solutions provide the most reliable and accurate results, as well as optimal scientific, economic, and operational outcomes. We work collaboratively with our customers on their journey to make a real difference in human lives. Our culture is founded on trust, respect, and uncompromising integrity, and we pride ourselves on being a dynamic and inclusive workplace that celebrates diversity and fosters innovation.

We can’t wait for you to join us as we continue our mission to improve the world around us. Information about Agilent is available at [Agilent.com](https://www.agilent.com).

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