Level

AstraZeneca

Product Owner, Omics & Data Platforms

AstraZeneca is hiring a Product Owner, Omics & Data Platforms in Barcelona, Spain. Level rates it ; you can apply on AstraZeneca.

Level's read on this job

  • On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
  • Tools and skills the posting cites include AI agents.
  • Level lists 1,200+ other open roles at AstraZeneca.
  • The role was posted in the last few days.

AI in this role

ai-agents

This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.


Role Summary

AstraZeneca's cancer and translational scientists rely on a chain of omics platforms to get from a sample to an insight: sample metadata management, omics processing, a molecular data warehouse, a cancer genomics portal, and single-cell and spatial analytics tools. We are seeking a Product Owner, Omics & Data Platforms, to own several of these platforms and to keep the chain working for the people who use it.


You will own the roadmap, vendor relationships and run quality for your platforms, starting with the cancer genomics portal and the single-cell and spatial analytics tools, and supporting the wider sample-to-insight chain. You will make sure the platforms follow FAIR data principles, are available to users around the world, and connect cleanly to the AI agents and tools that scientists increasingly use.


You will work with computational biologists, translational scientists, platform and data engineers, external vendors and open-source partners, procurement and finance. The ideal candidate combines a scientific understanding of omics data with sound product and vendor management, and is comfortable making decisions about what to keep, renew or replace.


Key Responsibilities


Product Ownership and Roadmap

  • Own the roadmap and backlog for the cancer genomics portal and the single-cell and spatial analytics platforms, and prioritise work with engineering and vendor partners.
  • Support the owners of the other platforms in the sample-to-insight chain, and keep a clear view of how data moves between them.
  • Maintain a current record of each platform's users, usage, cost and risks.

Vendor, License and Contract Management

  • Manage relationships with commercial vendors and open-source partners, and track delivery against agreed scope.
  • Prepare the case for renewals and changes to licenses, including usage, cost per user and alternatives, working with procurement and finance.
  • Help assess successor options in good time, so that contract end dates do not force a decision.

Platform Operations and Users

  • Make sure platforms meet agreed service levels, and work with engineering and support teams on incidents, releases and upgrades.
  • Run access, onboarding and training for users in different regions, and collect feedback to guide the roadmap.
  • Plan regional deployments or instances, for example for users in China or Japan.

Data Standards and Integration

  • Apply FAIR data principles and metadata standards across your platforms, working with data owners and platform engineering.
  • Define how platform data is exposed to AI agents and other tools through governed interfaces, with security and data owners.
  • Work through ownership gaps between platforms, such as sample identifiers and shared metadata.

Compliance, Privacy and Quality

  • Make sure platforms follow data privacy, access approval and data residency requirements, including controls for human-derived data.
  • Complete governance and AI reviews for platforms that add AI features.
  • Keep documentation current for audit and review.

Financial Stewardship

  • Track infrastructure, license and contractor cost against plan and flag overruns early.
  • Contribute to planning cycles with a clear view of run cost and options.

Cross-Functional Collaboration

  • Work closely with computational biologists, translational and oncology scientists, engineers, vendors, procurement and finance.
  • Communicate clearly about platform availability, limits and plans.

Desired Profile

Experience

  • 5 or more years of experience working with or managing omics data platforms or bioinformatics tools, ideally in a pharmaceutical or translational research setting.
  • Experience as a product owner or product manager, or in a role with equivalent ownership of a scientific platform.
  • Understanding of genomic, transcriptomic or single-cell and spatial data and how scientists use it.
  • Experience managing vendor relationships, licences and renewals, including working with procurement.
  • Fluency with FAIR data principles and metadata standards.
  • Working knowledge of cloud-based data environments and how scientific platforms are run and supported.
  • Understanding of data privacy and controlled handling of human-derived data.
  • Ability to communicate with both scientists and engineers.

Desirable

  • Experience with open-source scientific software and its communities.
  • Familiarity with AI agents and how they connect to scientific data.
  • Experience with oncology data.
  • Working knowledge of Atlassian tools (JIRA, Confluence).

#EAI

Date Posted

26-oct-2026

Closing Date

08-nov-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.

How we rate this

Product Owner, Omics & Data Platforms at AstraZeneca rates 24 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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