Azure Data Architect (AI Focus)

Svitla Systems Inc. is looking for Azure Data Architect (GenAI/RAG/Agentic focus) for a full-time position (40 hours per week) in Europe. Our client operates a fleet of kiosk terminals running Windows 11 IoT Enterprise with a single Unity application in Shell Launcher kiosk mode. You will take the existing master image to a release-ready state and then support and maintain it. This is Windows systems and imaging work - the Unity application itself is built and maintained by another team and is out of scope.

The project focuses on building a scalable and governed data platform to support analytics and reporting needs. The solution involves onboarding diverse SQL and non-SQL data sources, designing and developing data marts, and enabling reporting through Power BI.

As part of this initiative, the team will define the required data marts and migrate the existing database to Snowflake, helping establish a scalable and reliable foundation for future analytics and reporting.

Requirements

• 8+ years of professional experience in Data Engineering, with strong experience in enterprise data architecture and architecture ownership.
• Azure Cosmos DB - fundamental and essential; hands-on with data modeling, partition-key strategy, indexing, RU/throughput and cost optimization, scaling, consistency, and Change Feed.
• Real, production project experience where the candidate personally designed or contributed to the data architecture behind a RAG, GenAI, or Agentic AI application - must be clearly demonstrated through actual project ownership, not just a requirements/skills mapping.
• Experience designing AI-ready data architecture and the data layer around RAG/GenAI/Agentic applications (ingestion, preparation, storage, governance, and retrieval-readiness of enterprise data/knowledge).
• Strong enterprise Azure Data Architecture experience with proven architecture ownership - including inheriting a problematic legacy platform, separating tactical stabilization from long-term architecture, and evaluating technology choices based on scale/cost/business need rather than preference.
• Strong, current Azure Synapse Analytics experience across Pipelines, Spark, Serverless SQL, and Dedicated SQL Pools, with a clear understanding of where Synapse fits versus Azure SQL, ADF, ADLS, Databricks, or Fabric.
• Azure Databricks experience (data engineering and data warehousing), combined with the ability to stay technology-neutral - including recommending against Databricks/Synapse/ADF when not justified.
• Experience with Azure SQL MI / SQL Server / T-SQL.
• Experience with Azure Data Lake / Lakehouse architecture.
• Experience with Azure Functions.
• Strong data modeling (fact tables, dimensional/star schemas, analytical model optimization), ingestion design, and platform modernization ownership.
• Experience with structured and genuinely unstructured data end-to-end (documents, PDFs, scans, images, emails) - ingestion, processing, storage, and downstream usability.
• Metadata/configuration-driven ingestion and scalable, reusable data-platform design.
• Demonstrated ability to deliver fast: assess an undocumented platform and produce a concrete, actionable architecture plan within Week 1-2, working independently with incomplete information.
• Strong communication skills with both technical and business stakeholders.

Will be a plus

• Hands-on exposure to document processing, OCR, text extraction, chunking, embeddings, or vector search/retrieval technologies (as supporting evidence of RAG-related data layer work, not a standalone requirement).
• Familiarity with Azure Fabric as a point of comparison to Synapse/Databricks.
• Experience with dbt or similar transformation frameworks as an alternative to Databricks-based ETL.

Responsibilities

• Own and evolve the enterprise Azure data architecture, including taking over and assessing an existing, only partially documented data platform.
• Design the data layer/architecture that supports GenAI, RAG, and/or Agentic AI applications - defining how enterprise data and knowledge is ingested, prepared, stored, governed, and made available for retrieval and AI consumption (architecture design only - hands-on RAG/agent development is not required).
• Own Cosmos DB architecture decisions: data modeling, partition-key strategy, indexing, RU/throughput and cost optimization, scaling, consistency, and Change Feed usage.
• Define and evolve the data architecture across Azure Synapse Analytics (Pipelines, Spark, Serverless SQL, Dedicated SQL Pools), Databricks, Azure SQL MI/SQL Server, Data Lake/Lakehouse, and Azure Functions - making technology-neutral calls on what's actually needed versus what adds unnecessary complexity.
• Separate tactical/short-term fixes from long-term target architecture, and produce implementation-ready output (documentation, diagrams, recommendations) that engineering teams can act on directly.
• Design ingestion and processing of both structured and genuinely unstructured data (documents, PDFs, scans, images, emails), including how it's stored and made usable downstream.
• Design a metadata/configuration-driven, reusable ingestion framework that allows new sources (databases, APIs, files) to be onboarded without rebuilding from scratch each time.
• Quickly assess an existing/legacy environment and deliver concrete, actionable architecture recommendations within the first 1-2 weeks.
• Communicate and align architectural decisions with both technical teams and business stakeholders.