Data Engineer

<p><strong>Purpose of position</strong></p> <p>Data sits at the heart of the company. This role is to ensure that Awin can leverage the data it has access to across the group and build out reporting to ensure we are able to make better commercial decisions and provide best-in-class campaign management to support client-facing departments.</p> <p>As a <strong>Data Engineer</strong>, your role focuses on designing, building, and optimizing scalable data pipelines, curated datasets, and analytical data models within Azure, AWS, and Databricks environments. The position involves working with largescale datasets, improving performance and reliability, and translating business logic into well-structured tables, metrics, and transformation rules. The role also supports analytics teams through the delivery of high-quality, well documented datasets and ensures strong operational monitoring, data quality, and engineering best practices.</p> <p> </p> <p><strong>Key Responsibilities</strong></p> <p>Data Engineering & Pipelines</p> <ul> <li>Build and maintain ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Delta Lake.</li> <li>Develop production ready notebooks, workflows, and data lake integrations.</li> <li>Apply best practices for Spark optimization (partitioning, caching, avoiding shuffle, file compaction).</li> </ul> <p>Dataset Modeling & Business Logic</p> <ul> <li>Design curated datasets, semantic layers, and data marts that power analytics and reporting.</li> <li>Partner with business stakeholders to understand requirements, operational challenges and decision-making needs, ensuring alignment between business expectations and technical constraints.</li> <li>Convert business requirements into data models, defining tables, metrics, KPIs, and transformation rules.</li> <li>Work closely with product owners and analysts to align datasets with business processes.</li> <li>Document data models, lineage, logic, and dataset behaviour clearly and consistently.</li> </ul> <p>Performance, Monitoring & Reliability</p> <ul> <li>Work with very large datasets, optimizing transformations and storage for scale and cost.</li> <li>Tune and maintain PostgreSQL databases (indexes, running processes, performance diagnostics).</li> <li>Implement robust data validation, schema enforcement, and quality checks across pipelines.</li> </ul> <p>Teamwork & Delivery</p> <ul> <li>Collaborate with engineers, analysts, and business stakeholders to deliver reliable data solutions.</li> <li>Communicate effectively with both technical and non‑technical audiences.</li> </ul> <p> &l