Senior Data & AI Architect

Zürich, Switzerland

Actively hiring, first seen by us today

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting

Design the Data and AI operating model, roles and governance
Define the target data platform architecture, including GenAI components
Run make-or-buy analyses and vendor selections with business cases and TCO
Gather functional requirements and turn them into data products with SLAs
Act as design authority, reviewing system integrator solutions during rollout

From the employer’s posting

Design the Data & AI operating model (centralized, federated, hub-and-spoke) following Data Mesh principles

Define the target data platform architecture on cloud (Azure, AWS, GCP) and Databricks, including GenAI components (RAG, agents, vector stores, LLMOps).

Run make-or-buy analyses and vendor selections: scouting, evaluation criteria, business case, TCO and final recommendation.

Gather and formalize functional requirements with the business, translating them into data products with owners, consumers, SLAs and quality metrics.

Act as design authority during implementation: review system integrator solutions and oversee quality, progress and risks of the rollout.

What you’ll bring

All qualifications

Core experience

Hands-on experience designing operating models, Data/AI organizations or CoEs.
Experience with clients in pharma sector.
Experience with make-or-buy analyses, vendor selection or target architecture definition.
Experience in tech due diligence or post-merger transformation projects.

Qualification wording

Hands-on experience designing operating models, Data/AI organizations or CoEs.

Experience with clients in pharma sector.

Experience with make-or-buy analyses, vendor selection or target architecture definition.

Experience in tech due diligence or post-merger transformation projects.

Apply at LinkedIn Job Wrapping

Posting record

Report an error

Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 5, 2026today
Recorded sightings: 1

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 7 and day 54.

At LinkedIn Job Wrapping, jobs usually come down by about day 62.

From 7,977 similar postings that came down, and 19 of LinkedIn Job Wrapping’s. Coming down is not always a hire.

LinkedIn Job Wrapping is hiring

24 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring at a steady pace
Its jobs tend to stay up a long time

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

AWS
Azure
Databricks
Delta Lake
GCP

Where the posting names them

Architecture and technology choices - Define the target data platform architecture on cloud (Azure, AWS, GCP) and Databricks, including GenAI components (RAG, agents, vector stores, LLMOps). - Run make-or-buy analyses and vendor selections: scouting, evaluation criteria, business case, TCO and final recommendation.

- Data Mesh and governance: domain ownership, data products, federated governance, data contracts, catalogues and lineage (e.g. Unity Catalog, OpenMetadata, Collibra). - Cloud: data and AI PaaS services on at least one of Azure, AWS and GCP; security, networking and FinOps principles. - Databricks: Lakehouse, Delta Lake, Unity Catalog, Workflows; ability to assess design and cost.

- Databricks: Lakehouse, Delta Lake, Unity Catalog, Workflows; ability to assess design and cost. - GenAI: RAG and agentic architectures, LLMOps, model evaluation, platforms such as Azure AI Foundry, Vertex AI and Bedrock. - Operating model: Data/AI organization, CoE, roles (data owner, data steward, product owner), demand and portfolio processes.

- Experience with clients in pharma sector. - Cloud or Databricks certifications (e.g. Azure Solutions Architect, Databricks Data Engineer or Solutions Architect). - Experience in tech due diligence or post-merger transformation projects.

- Cloud: data and AI PaaS services on at least one of Azure, AWS and GCP; security, networking and FinOps principles. - Databricks: Lakehouse, Delta Lake, Unity Catalog, Workflows; ability to assess design and cost. - GenAI: RAG and agentic architectures, LLMOps, model evaluation, platforms such as Azure AI Foundry, Vertex AI and Bedrock.

Upload resume Add LinkedIn

Your next step

Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.

Applying through Greenhouse

No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.

Already applied? Track this application

Demand for Data Leadership roles

New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.

August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Leadership, by the work the posting describes.

Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.

Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.

Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.

US government figures for computer and information systems managers

Median yearly pay, May 2025: $175,140
Expected change, 2025 to 2035: 15.8% more jobsagainst 3.5% for all jobs

The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.

Job description

View original posting ↗

Who/What is Artefact?
Artefact is a next-generation strategy and data consulting firm dedicated to transforming organizations through data and AI. We combine the rigor of top-tier strategy consulting with deep expertise in data, digital, and analytics to help clients achieve tangible
business impact.
With 1,800+ consultants, data scientists, and engineers across 23 countries, we work with global leaders such as Samsung, L’Oréal, Orange, and Sanofi. Our Zurich office is at the heart of Artefact’s growth, advising clients on their most pressing strategic challenges — from AI strategy and governance to digital transformation roadmaps and new business model design.

The role:

We are looking for a Senior Data & AI Architect who combines business strategy and technology across Data and GenAI. You will design the operating model and the organization of the Data & GenAI CoE, define the target architecture, and steer data products from demand through to rollout.

Key responsibilities:

Operating model and organization

Design the Data & AI operating model (centralized, federated, hub-and-spoke) following Data Mesh principles: roles, responsibilities, domain ownership and governance mechanisms.
Define the organization of the Data & GenAI CoE: scope, service catalogue, skills, sizing and engagement model with business domains.
Design end-to-end processes for demand management, use case prioritization, funding and value tracking.

Architecture and technology choices

Define the target data platform architecture on cloud (Azure, AWS, GCP) and Databricks, including GenAI components (RAG, agents, vector stores, LLMOps).
Run make-or-buy analyses and vendor selections: scouting, evaluation criteria, business case, TCO and final recommendation.
Check that architecture, security and data governance choices are consistent with enterprise standards and with the operating model.

Data products: from demand to rollout

Gather and formalize functional requirements with the business, translating them into data products with owners, consumers, SLAs and quality metrics.
Design data products from both a functional angle (use cases, KPIs, impacted processes) and a technical one (data model, pipelines, interfaces, serving layer).
Act as design authority during implementation: review system integrator solutions and oversee quality, progress and risks of the rollout.

Requirements:

At least 4 years of professional experience in IT.
At least 2 years on projects focused on AI/GenAI, Data Mesh and functional requirements gathering.
Consulting experience, ideally in strategy consulting, combined with experience at a system integrator or delivery firm.
Hands-on experience designing operating models, Data/AI organizations or CoEs.
Experience with make-or-buy analyses, vendor selection or target architecture definition.
Excellent written and spoken English.

Technical and functional skills:

You will not be expected to write code, but you need to know the technologies well enough to assess them, challenge vendors and spot a flawed setup.

Data Mesh and governance: domain ownership, data products, federated governance, data contracts, catalogues and lineage (e.g. Unity Catalog, OpenMetadata, Collibra).
Cloud: data and AI PaaS services on at least one of Azure, AWS and GCP; security, networking and FinOps principles.
Databricks: Lakehouse, Delta Lake, Unity Catalog, Workflows; ability to assess design and cost.
GenAI: RAG and agentic architectures, LLMOps, model evaluation, platforms such as Azure AI Foundry, Vertex AI and Bedrock.
Operating model: Data/AI organization, CoE, roles (data owner, data steward, product owner), demand and portfolio processes.
Requirements and delivery: requirements gathering, user stories, process modelling, agile methods, programme governance.
Business: business cases, TCO, value tracking, and the ability to translate technical choices into economic impact.

Nice to have:

German not strictly required.
Experience with clients in pharma sector.
Cloud or Databricks certifications (e.g. Azure Solutions Architect, Databricks Data Engineer or Solutions Architect).
Experience in tech due diligence or post-merger transformation projects.
Prior technical background as an engineer or developer.

What we offer:

A role bridging strategy and technology, with direct impact on client decisions.
International projects on Data Mesh, data platforms and GenAI.
Close collaboration with the Project Lead and a growth path towards programme leadership roles.
Location and working model: (Hybrid) Zurich or Lausanne.
Highly competitive compensation package

This position is based in Zürich.

Don’t worry if you don’t meet all the criteria! We value motivation, pragmatic problem-solving, and potential above all else. We’d love to hear from you and learn more about your passion for driving the AI revolution.

Source details