IT Data&Analytics Architecture unit is happy to welcome a new team member. As a Data Architect, you will provide the end-to-end technical vision and hands-on guidance, ensure that data assets, internal and external, are stored, moved, transformed, and exposed in a way that is, secure, governed, cost-efficient, and ready for analytics, AI/ML, and operational workloads. This role is more technically oriented, however you will also interact with the business stakeholders and translates strategy into a data architecture, coach squads on best practices.

What are you going to do?

• Develop and continuously refine the target data-platform architecture, including data lake, data warehouse, and operational data stores aligned with enterprise architecture standards
• Maintain current state and roadmap blueprints that cover storage, processing, metadata, security, and cost optimization
• Help to design and supervise robust, reusable pipelines for ingestion, transformation, and orchestration (Azure Data Factory / Databricks)
• Partner with ML Engineers on Databricks MLflow, feature tables, and model deployment pipelines
• Translate complex data platform topics into business language for non-IT stakeholders
• Create artefacts (roadmaps, OKRs, etc.)

Data Mesh

• Define the concept of "data product" for the Baltic organization in line with the group strategy
• Drive squads to build, publish, and maintain data contracts
• Monitor data contract compatibility and breaking changes
• Liaise data contract with Information Architects on naming, lineage, and business semantics

Technical Leadership

• Embed with development squads as the "data face" of architecture
• Review designs
• Troubleshoot issues
• Coach engineers on patterns and anti-patterns.
• Interact with the Group data architects

• Microsoft Azure: ADLS Gen2, Data Factory, Databricks (incl. Delta Lake & MLflow)
• Relational: SQL Server
• Search/NoSQL: Elasticsearch (index modelling, relevance tuning).
• Programming/Scripting: Python (PySpark, Pandas, etc.) and strong SQL
• DevOps: Git, CI-CD (Azure DevOps or GitHub Actions), Terraform

Architecture Know-How regarding:

• Lakehouse and warehouse patterns; data-mesh/data-product thinking
• Streaming vs batch design; micro-(and swarm-) services
• Data modelling (star/snowflake, event-driven schemas, etc.)

AI/ML Collaboration:

• Feature-store design, ML-ready data curation, model observability, and MLOps principles

Communication & Leadership skills:

• Excellent communication skills (especially in English to communicate with colleagues from different countries)
• Ability to influence across roles-engineers, product managers, executives
• Workshop facilitation, documentation, mentoring, and conflict resolution