Senior Data Engineer

Job Summary

The Senior Data Engineer role is a unique opportunity to help build a world‑class global data platform. You’ll be responsible for delivering products for our internal customers, designing, implementing, and maintaining complex data pipelines and infrastructure that will give RES a competitive edge. The Senior Data Engineer will be required to build relationships and collaborate with key stakeholders.

• Architect build, implement and operate reliable, secure and observable data pipelines and curated datasets that power enterprise reporting, analytics and AI/ML use cases.
• Take ownership of engineering quality, automation and performance across the data platform, partnering with architecture, governance, modelling and reporting teams to deliver end‑to‑end data products.
• Lead and directly line manage the data engineering team driving a high performing team, accountability, innovation and the latest technology for automation, cost efficiency and innovation.
• Implement robust engineering standards and lead delivery of and oversee curated datasets with strong data quality controls and monitoring. Drive observability and optimise performance and cost.
• Lead work across IT leadership, business domains and source system owners and integration teams to onboard new sources and validate extracts.
• Lead incident response strategy and implementation and continuous improvement for BAU operations; drive root cause remediation.
• Drive technical decisions, evaluate new technologies and tools, and contribute to the continuous improvement of our data ecosystem.
• Build our Data Platform and solve complex data problems to deliver insights helping to build our trading data platform.
• Foster a culture of sharing, mentor junior engineers, provide guidance, and drive excellence.
• Convert data engineering and architecture best practices in the Data and Engineering teams, building a data culture based on the highest quality and reliability standards.
• Lead AI/ML use cases by producing feature‑ready datasets and supporting data scientists with efficient data access patterns.

• Leadership (this is a senior role where your leadership and mentorship skills are important to the success of the wider team).
• Advanced skills in Microsoft stack including Azure, Fabric, Synapse, Purview and Power BI.
• Expert in data engineering tools and technologies including SQL, Python and others.
• Strong problem‑solving skills, operational mindset and ability to mentor, lead, train and QA engineers work.
• Innovation in the latest technologies for best practice engineering, including data and AI/ML.
• Strong, clear communication and stakeholder management and reporting of risk, change and impact.
• Enthusiastic about helping juniors develop and grow.
• The ability to thrive in this role which demands technical and data‑driven results.
• Skilled in data engineering for AI/ML initiatives.

• Degree in a relevant field directly related to data.
• Significant experience delivering enterprise‑grade data engineering solutions in production environments and leading teams to deliver high performance outcomes.
• Proven experience as a Senior Data Engineer, with a strong portfolio of building real‑time data systems and teams using modern approaches.
• Extensive experience of working with Data and Analytics teams that provide in‑depth analytics reporting capabilities across the business.
• Previous experience leading a small engineering team is essential, as is hands on current technical engineering experience.
• Extensive experience with Python including open‑source data libraries and frameworks and messaging systems, along with proficiency in building out modern data warehouses.
• Azure mastery:
  Deep expertise in the Azure Ecosystem, specifically Azure Data Factory (ADF), Synapse, Unity Catalogue, Fabric, Purview: data lakes/lakehouse, data quality and testing frameworks and automation.
• Leading and maintaining security and privacy by design: access controls, handling of PII, auditability.
• Working knowledge of AI and machine learning data requirements.
• Demonstrated ability to lead and improve reliability, cost, performance and maintainability of data…