Data Platform Engineer

About Graitec Group Graitec is a global leader in Building Information Modeling (BIM) solutions, designing and developing software that helps architects, engineers, and manufacturers design smarter and better. With over 30 years of innovation and an entrepreneurial spirit, we’ve tripled our revenue in just five years. Our North Star is clear — accelerate the digital transformation of the AECO industry and model the future. We achieve this by growing our recurring revenue through innovative software and services that drive adoption, integration, and lasting value for our customers. Our 800 experts across 30+ offices in 12 countries support more than 270,000 users worldwide. As a global Autodesk Platinum Partner, we combine world-class partnerships with our own cutting-edge software and services to drive performance and sustainability across the industry. At Graitec, we move fast and think big. We collaborate across teams and borders, embrace diversity, and challenge ourselves to innovate every day. We believe in doing the right thing, breaking down silos, and making an impact together. How we work: Growth, Agility, Innovation, Responsibility How we behave: Ambition, Engagement, One Graitec, Positive Energy Learn more about the Graitec Group: graitec-group.com/graitec-a-global-player About the Team Hiring IT & Business Transformation is a global function reporting to the Graitec CEO, made up of four teams: Business Transformation, Data & AI, Information Security, and IT Operations & Platforms. Together we own the delivery capability behind how Graitec operates and we drive Graitec’s continued transformation. We work as fusion teams and as one with the wider organisation: technology and business colleagues working towards the same goals and outcomes. Data & AI is the team that turns Graitec's data into decisions. It brings together data platform engineering, BI and machine learning development, data science, AI engineering, and business-aligned analysts embedded with the functions they serve. The team is growing, and you will help shape how it works. Overview Business Purpose & Value Contribution 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, modelled 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 What success looks like in the first 12 months 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 Platform Engineering 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 Reliability & Cost 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 Security & Governance Implement access, classification, row- and column-level security, masking and privacy controls with Information Security Protect sensitive data such as compensation, using anonymisation and restricted workspaces Maintain lineage and documentation, supporting the data governance the business agrees Collaboration 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 Responsibilities 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 modelling 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. Qualifications Strong proficiency in SQL and Python/PySpark development. Expertise in analytics-focused data modelling and data architecture. Deep knowledge of Microsoft Fabric technologies, including: Lakehouse Warehouse Data Pipelines Notebooks 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) 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) Familiarity with modern AI data architectures, including: Retrieval-Augmented Generation (RAG) Vector Search MCP-enabled data access and governance Knowledge of modern data engineering ecosystems, including: dbt Snowflake Databricks Infrastructure as Code (IaC) practices. Interview Process At Graitec, we’re proud to foster a diverse and inclusive workplace. We value our employees for who they are and the contributions they bring, encouraging everyone to be their authentic selves at work. This diversity helps us better serve the wide range of customers and markets we operate in. We welcome applications from all backgrounds and assess candidates solely on their skills and ability to succeed in the role. Graitec uses AI to support and streamline internal processes; however, all application reviews, screening, and hiring decisions are made solely by our Talent Acquisition team and hiring managers. HR Screening – An initial “get to know you” discussion. Technical Assessment Interview – May include a presentation or a collaborative exercise. Hiring Manager Interview – A role-specific discussion with the hiring manager. Final Interview – Conversation with the Business Unit Leader. Additional Information As a signatory of United Nations Women’s Empowerment Principles, Graitec is committed to equal opportunity and pay transparency. On Target Earning pay range for this role Role Targeted Variable Pay Mix: 10% Minimum Annual On Target Earning: € 40,000 Maximum Annual On Target Earning: € 60,000 Note: Salary range indicated in this add is for information only & targeted for a specific seniority level and targeted location. Final salary offer might vary depending on final candidate location, seniority, competencies... 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 modelling 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. Strong proficiency in SQL and Python/PySpark development. Expertise in analytics-focused data modelling and data architecture. Deep knowledge of Microsoft Fabric technologies, including: Lakehouse Warehouse Data Pipelines Notebooks 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) 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) Familiarity with modern AI data architectures, including: Retrieval-Augmented Generation (RAG) Vector Search MCP-enabled data access and governance Knowledge of modern data engineering ecosystems, including: dbt Snowflake Databricks Infrastructure as Code (IaC) practices.