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 focus—blending architecture, automation, and data engineering—to empower s next generation of AI and BI capabilities.

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

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).

• 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 SME—guiding system administrators and upskilling junior technical team members.

Months 3-12:

• 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.

• 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 2-3 days/week onsite in Cardiff office .

• Development: Funded certifications, dedicated R&D time, access to Company networks and resources.