[Hiring] Data Platform Engineer @Graitec

Role Description

The Data Platform Engineer builds and runs the platform that all of Graitec's reporting, analytics, and AI depend on: the Microsoft Fabric data lake, the pipelines that feed it, and the controls that keep it reliable, secure, and affordable. The priority is a governed single source of truth: data from Dynamics 365 Finance & Operations, CRM, services licensing, and product telemetry, integrated once, modeled consistently, and served to every function, with sensitive data protected by design.

You will work with the Data and AI Leader, BI developers, IT Operations & Platforms, Information Security, and R&D. It is a hands-on senior engineering role, setting the patterns others build on.

Key Outcomes Expected

• A published data platform architecture and engineering standards, with a layered data model in use.
• Core sources ingested reliably and monitored, with failures detected before users notice.
• Platform capacity right-sized and its cost transparent, with workloads separated so reporting and engineering do not collide.
• Access, classification, and privacy controls in place, including for HR and financial data.
• Pipelines and models in source control, with automated deployment across environments.
• A clear interface with R&D for product telemetry and shared data platforms.

Responsibilities

• Design, build, and run the Microsoft Fabric platform: lakehouses, warehouses, pipelines, and dataflows.
• Ingest and integrate data from Dynamics 365, CRM, Kantata, licensing, and telemetry, using standard connectors and APIs where they fit.
• Build a layered, medallion-style data model that turns raw data into certified, reusable datasets.
• Resolve customer and product identities across CRM, ERP, licensing, and telemetry, so every report counts the same customer once.
• Monitor pipelines and automate data quality tests for completeness, freshness, uniqueness, and referential integrity, with alerting and clear ownership of failures.
• Manage platform capacity and cost, separating workloads and scaling only when justified.
• Run source control, environments, and deployment pipelines for data and BI content.
• Implement access, classification, row- and column-level security, masking, and privacy controls with Information Security.
• Protect sensitive data such as compensation, using anonymization and restricted workspaces.
• Maintain lineage and documentation, supporting the data governance the business agrees.
• Set engineering standards and coach BI developers and analysts in using them.
• Work with the Enterprise Architect and R&D on integration patterns, master data, and telemetry.
• Prepare data for AI, exposing governed, well-described data to agents and models through controlled interfaces.

Qualifications

• 6+ years of experience in Data Engineering, including at least two years working with Microsoft Fabric, Azure Synapse, or Databricks.
• Strong experience with SQL and Python or PySpark, and data modeling for analytics.
• Hands-on experience with Microsoft Fabric, including Lakehouse, Warehouse, Pipelines, Notebooks, and OneLake.
• Experience integrating ERP and CRM data, ideally from Dynamics 365 Finance & Operations and Dataverse.
• Experience with master data management and entity resolution, including matching customers and products across multiple systems.
• Practical experience implementing security and governance, including Entra ID, row-level security, and Microsoft Purview.
• Experience managing capacity and costs on a shared analytics platform.
• Uses AI tools such as Copilot in day-to-day work for code generation, testing, and documentation.
• Strong communication skills, with the ability to document effectively and explain technical trade-offs to non-engineering stakeholders.
• Experience with product telemetry or event-driven data.
• Experience preparing data for AI solutions, including retrieval, vector search, and governed access for agents (for example through MCP).
• Experience with other modern data platforms and tools such as dbt, Snowflake, or Databricks, and with infrastructure as code.

Requirements

• Strong proficiency in SQL and Python/PySpark development.
• Expertise in analytics-focused data modeling and data architecture.
• Deep knowledge of Microsoft Fabric technologies, including Lakehouse, Warehouse, Data Pipelines, Notebooks, and OneLake.
• Experience with source control and CI/CD practices for data platforms, including Git and Azure DevOps.
• Knowledge of enterprise security, governance, and compliance frameworks, including Entra ID, Row-Level Security (RLS), and Microsoft Purview.
• Understanding of data platform operations, including performance, scalability, and cost optimization.
• Practical application of AI-assisted development tools such as Copilot.

Preferred Certifications

• Microsoft Fabric Data Engineer (DP-700)
• Microsoft Fabric Analytics Engineer (DP-600)

Benefits

• Familiarity with modern AI data architectures, including Retrieval-Augmented Generation (RAG), Vector Search, and MCP-enabled data access and governance.
• Knowledge of modern data engineering ecosystems, including dbt, Snowflake, Databricks, and Infrastructure as Code (IaC) practices.