_{03rd June, 2026}

About the Role

We're partnering with a fast-growing, private capital-owned SaaS company in a massive growth phase to find a pivotal Data Engineer. Reporting directly to the Engineering Manager, you will sit at the intersection of data engineering, analytics, and applied AI — building the data infrastructure that powers business decisions, customer experiences, and AI-driven outcomes.
This is a high-autonomy role on a platform in active evolution. You'll own the full data lifecycle — ingestion, transformation, storage, modelling, and consumption — and be trusted to build infrastructure that is scalable, secure, maintainable, and AI-ready.

What You Will Do

• Pipeline Engineering: Design, build, and maintain reliable, scalable data pipelines from diverse internal and external sources — applying software engineering rigour including version control, testing, and CI/CD.
• Data Quality & Curation: Own the integrity of data assets end-to-end, establishing standards, managing lineage and cataloguing, and resolving quality issues at root cause — not just symptoms.
• Analytics & Reporting: Develop dashboards (Power BI), semantic layers, and self-serve data models that deliver clear, actionable insights for internal teams and external customers.
• AI & ML Enablement: Build and maintain infrastructure supporting AI/ML use cases — feature stores, training datasets, embeddings, and inference pipelines — and apply AI tooling to the data engineering work itself.
• Snowflake Management: Manage and optimise data workloads including warehouse sizing, clustering, and cost efficiency.
• Governance & Security: Apply access controls, masking, and encryption practices appropriate to each dataset, ensuring compliance with privacy, retention, and audit obligations.
• Platform Evolution: Actively support the migration from PHP to .NET, ensuring data integrations and pipelines evolve alongside the application platform.

What You Will Bring

• Experience: 6–8+ years in data engineering, analytics engineering, or closely related roles.
• Snowflake Expertise: Proven hands-on experience with Snowflake (or equivalent), including data modelling, query optimisation, and platform management.
• Engineering Rigour: Solid software development background with .NET (C#) highly valued; strong SQL skills across structured, semi-structured, and unstructured data.
• Pipeline Tooling: Hands-on experience with orchestration tools such as Apache Airflow or dbt, and modern ELT/ETL approaches.
• Data Modelling: Deep understanding of modelling patterns — dimensional, Data Vault, or similar — and when to apply them.
• AI Curiosity: Practical experience applying AI and ML techniques to data problems, with genuine interest in staying current as the space evolves.
• AWS Cloud: Experience on cloud data platforms, with AWS as the primary environment.
• Collaborative Mindset: You translate ambiguous business questions into structured, deliverable data outcomes — and build trust with engineering, product, and business stakeholders alike.

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