Senior Data Architect (Azure / GenAI-Ready Data Platform)

Type: Full-time, remote

Location: EU + CIS + Balkans (citizenship doesn't matter — what matters is current location and the jurisdiction from which access to client servers would occur)

Contract: 12 months, long-term, with potential for extension

Start date: Early September 2026 (ASAP)

Time zone: CET

English: B2 minimum, C1 preferred

About the Role

We're looking for a senior Data Architect to design and modernize an Azure data platform, including the data architecture underpinning GenAI/RAG/Agentic applications. This is an architecture role, not hands-on RAG/ML development — the focus is on how enterprise data and knowledge are ingested, prepared, stored, governed, and made available for retrieval and AI consumption.

Must-Haves

Azure Cosmos DB — hands-on experience with data modeling, partitioning, indexing, RU/throughput and cost optimization, consistency, Change Feed

Proven experience designing data platforms supporting GenAI/RAG use cases

AI-ready data architecture experience

A real project where the candidate designed or contributed to the data architecture supporting a RAG, GenAI, or Agentic AI application

Azure Synapse Analytics — depth across workload patterns (Pipelines, Spark, Serverless SQL, Dedicated SQL Pools)

Azure Databricks

Azure SQL MI / SQL Server / T-SQL

Azure Data Lake / Lakehouse architecture

Azure Functions

Experience with structured and genuinely unstructured data (documents, PDFs, scans, images, email, free text)

Metadata/configuration-driven ingestion — reusable, scalable design for onboarding new data sources

Demonstrated architecture ownership — real decisions made, not just pipeline implementation

At least 4 years of recent (not dated) hands-on Azure experience

Must be an Architect by role, not a Data Engineer

What You'll Do

Take over an existing, problematic data platform, quickly assess it, and deliver both tactical fixes and a longer-term target architecture

Design a reusable ingestion architecture for heterogeneous sources (databases, APIs, files)

Make technology-neutral calls on when Databricks/Synapse/ADF are actually needed — and when they're not

Produce detailed, implementation-ready architecture documentation for engineering teams

Own data governance, data quality, and incremental processing

Communicate effectively with both technical and business stakeholders

A Note on GenAI/RAG

You won't be building RAG solutions or AI agents yourself. What's required is experience designing the data architecture around such applications — how enterprise data and knowledge get ingested, processed, indexed, and made available for retrieval and AI consumption (document processing, OCR, chunking, embeddings, vector search, etc.), including where Cosmos DB fits into that picture.

Interview Process

Intro call

Technical interview (~1 hour)

Be ready to walk through 2–3 concrete project examples using: problem → options considered → your decision → rationale → implementation → result. The focus is less on definitions and more on how you think as an architect and how quickly you can turn a messy existing situation into practical direction for an engineering team.