Инженер данных

Agilent Technologies provides life science, diagnostic, and applied market laboratories worldwide with instruments, services, consumables, applications, and measurement and asset management expertise.

• Build domain data products and pipelines for pod use cases according to data plane certification standards;
• Define semantic metadata, data contracts, entitlement metadata, agent identity, lineage, and certification tiers;
• Score data quality against defined dimensions and provide quality signals for evaluation systems;
• Work with domain data owners and stewards to ground retrieval in validated definitions and certified sources;
• Build structured and unstructured retrieval foundations, including vector and graph assets where required;
• Deploy agents for metadata generation, entity resolution, and content classification;
• Document reusable domain data products and hand them to the data plane for certification and enterprise reuse;
• Develop domain data models with business teams and IT;
• Create data collection frameworks for structured, unstructured, and disconnected data from internal and external sources;
• Establish dataset processes and build data structures based on business and technical requirements;
• Consult on design standards and assurance processes for software, systems, applications, data connections, flows, and storage;
• Create data tools for analytics and data science teams;
• Prepare and manipulate data for predictive and prescriptive modeling;
• Identify automation opportunities and improve data reliability, efficiency, and quality;
• Review business and product requirements for data operations and suggest system and storage upgrades;
• Deliver contract-governed and quality-scored data products with no undocumented side channels into source systems;
• Certify multiple domain data products in the registry for reuse by multiple use cases;
• Ensure domain quality signals flow into the evaluation system and gate agent behavior when quality thresholds are not met;
• Improve data-to-build time through reuse and process optimization.

• Strong data engineering experience building retrieval-ready, semantically annotated, contract-governed data products for AI consumption;
• Hands-on familiarity with Microsoft Fabric, Snowflake, vector stores, and graph stores;
• Experience working with data contracts, lineage, and certification requirements;
• Experience with RAG data foundations, including chunking, embedding, hybrid retrieval, and agent-related failure modes;
• Ability to work within a business domain and collaborate with data stewards;
• Curiosity about AI potential and failure modes;
• History of deliberate self-reinvention and continuous learning;
• Excellent written and verbal communication and ability to influence stakeholders;
• Ability to adapt communication for audiences ranging from bench scientists to board members;
• Bachelor's or Master's Degree or equivalent;
• At least 8+ years of relevant experience.

• Pay ranges depend on role, level, and location; individual pay also depends on work location, skills, experience, and relevant education or training;
• Pay and benefit information is available by country;
• Travel required 10% of the time;
• Day shift;
• No end date.