Senior Data Architect, Scientific Data Platform

Agilent instruments produce the measurements behind pharmaceutical quality control, food and environmental testing, and research labs worldwide. That data sits in dozens of software products, and we are building a platform that lets our products, our customers' workflows, and AI agents find and use it wherever it lives.

We are hiring a data architect to own the discovery and access side of that platform: how scientific data is registered and found, and how it is composed and served. The platform architecture is established; the target state for discovery and access, and the path to it, are yours to define.

You work under the Lead Data Architect, who owns cross-service standards and governance. Within your areas, the architecture decisions are yours; cross-service decisions go to the Lead with evidence, options, and a recommendation.

This is an architecture role with hands-on evidence: you decide the architecture for your areas, stay with engineering through implementation, and read the code, run the queries, and build the proof of concept when an argument needs one.

• Establish how the catalog and access paths behave today and define the target state: service boundaries, contracts, integration patterns, and the path between the two.
• Own the schemas and contracts your areas depend on: catalog records, subgraph boundaries, data service views, and versioned event schemas. Design them and make explicit which system is authoritative for each piece of information under the program's data governance rules, and version them so existing consumers keep working.
• Design the access paths AI agents use to read what they can, under what controls, and with what provenance.
• Review proposed integrations against your contracts and the program's participation requirements. Change your contracts when the evidence warrants and propose changes to the requirements.
• Design modernization paths for legacy sources that keep customers running through the transition, including moves from on-premises to hybrid or cloud.
• Set query and index performance targets for catalog and access paths.
• Design the pipelines that keep the catalog, the search index, and the access views current as data arrives, from extractor output through backfill and recovery.
• Work directly with engineering: clarify designs, evaluate implementation plans, review delivered work against the decisions.
• Write the decision records and diagrams for your areas, and explain the tradeoffs to engineers, product managers, and business stakeholders.

25% of the Time

Day

No End Date

• Data access. Your part of the federated GraphQL gateway, which the platform team owns how connected systems are represented on it, directly or through data services; the read layer that composes results across systems; and the application data services that expose access-controlled views of scientific data to products, workflows, and AI consumers.
• Catalog and search. The catalog record model, how connected systems register their data and how that metadata is represented, search over it, and the path to richer indexes like vector and graph.

Access is the first-year priority; catalog work runs alongside as the first connectors land. File management, metadata extraction, and semantic standards are owned by other architects on the team. The data is scientific data created by software products: results, methods, samples, files. ERP, CRM, and other enterprise business systems are out of scope.

• .NET (C#) services, Angular front ends, and a federated GraphQL gateway
• PostgreSQL by default, with Oracle and SQL Server across the installed base
• RabbitMQ for messaging and event-driven change discovery
• Kubernetes and Helm alongside Windows installers for on-premises and hybrid customers, with S3-compatible object storage
• Scientific data in proprietary instrument formats as well as vendor-neutral formats like Allotrope
• AI assistants in daily use