The Role We are looking for a hands-on Azure Data Engineer who will lead the final phase of our Client's cloud migration and design the enterprise-grade data platform from the ground up. This is a hybrid role with a strong technical focusblending architecture, automation, and data engineeringto empower s next generation of AI and BI capabilities. About The Company The Company is a dynamic, global procurement consultancy operating across Europe, the US, and APAC. As they scale globally and accelerate their AI capabilities, they are completing their transition to the cloud and building a company-wide data platform to power insight-driven transformation for their consultants and clients Required Skills & Experience Must-Haves: 3 years of hands-on Azure engineering experience (IaaS ? PaaS), including Infra as Code. Strong SQL skills and proficiency in Python or PySpark. Built or maintained data lakes/warehouses using Synapse, Fabric, Databricks, Snowflake, or Redshift. Experience hardening cloud environments (NSGs, identity, Defender). Demonstrated automation of backups, CI/CD deployments, or DR workflows. Nice-to-Haves: Experience with Azure OpenAI, vector databases, or LLM integrations. Power BI data modeling, DAX, and RLS. Certifications: AZ-104, AZ-305, DP-203, or AI-102. Knowledge of ISO 27001, Cyber Essentials, or SOC 2 frameworks. Exposure to consulting or professional services environments. Familiarity with the Power Platform. Awareness of data privacy regulations (e.g., GDPR, CCPA). Soft Skills Consultative mindset can turn business questions into technical outcomes. Comfortable switching hats: architect, hands-on builder, and mentor. Clear communicator, able to work effectively across time zones and teams. Thrives in a small, high-trust, high-autonomy team culture. Day-to-Day Responsibilities Infrastructure & Automation: Deploy and manage infrastructure using Bicep/Terraform, GitHub Actions, and PowerShell/DSC. Data Engineering: Architect and implement scalable ETL/ELT solutions; model schemas, optimize performance, and apply lakehouse best practices. Security & Resilience: Implement best-practice cloud security (NSGs, Defender, Conditional Access), automate DR/backups, and run quarterly restore drills. Collaboration: Partner with AI Product Owners, Business Performance, and Data Analysts to translate business needs into robust data solutions. Mentorship & Knowledge Sharing: Act as a data SMEguiding system administrators and upskilling junior technical team members. What You'll Achieve in Year 1 Months 312: Design and build their Azure data lake using Synapse, Fabric, or an alternative strategy. Ingest data from core platforms: NetSuite, HubSpot, and client RFP datasets. Automate data pipelines using ADF, Fabric Dataflows, PySpark, or SQL. Publish governed datasets with Power BI, enabling row-level security (RLS). By Year-End: Deliver a production-ready lakehouse powering BI and ready for AI/Gen-AI initiatives. Position the business to rapidly scale data products across regions and services. Whats in It for You Greenfield opportunity: Shape and deliver the first enterprise data platform. Career growth: Scale with the company into Lead Data, Cloud, or Solution Architect roles. Hybrid flexibility: Remote-first with 23 days/week onsite in Cardiff office. Development: Funded certifications, dedicated R&D time, access to Company networks and resources.