Data Engineer

Data Engineer | Permanent Full-Time | London Hybrid | Up to £65,000 per annum

Peaple Talent have partnered with a leading consultancy in the automation space looking to recruit a Data Engineer on a full-time permanent basis in London.

Our client is expanding their Automation and Analytics capability and is looking for an experienced Data Engineer to help shape and scale their data landscape. This position focuses on creating reliable, high-performance data workflows within a mixed on-premise and cloud-based environment, while continuously improving the underlying data platform to meet the needs of varied and evolving engagements.

You will be embedded in a multidisciplinary team, collaborating with analytics specialists, engineers, and client-facing consultants to deliver data-driven solutions. The role offers exposure to a broad range of data types and systems, primarily supporting a multinational enterprise operating across sectors such as energy, natural resources, manufacturing, food production, infrastructure, and transportation throughout Europe.

This role suits someone who takes initiative, adapts quickly, and thrives in a setting where priorities shift and multiple platforms must be supported simultaneously. Success in this position requires balancing hands-on delivery with a strategic mindset, ensuring data solutions remain scalable, consistent, and fit for purpose across different clients and use cases.

Key Responsibilities:

• Assume full responsibility for legacy and in-flight data solutions, overseeing their reliability, troubleshooting issues, implementing improvements, and ensuring long-term stability.
• Work closely with stakeholders to understand operational goals, assess current platforms and data movements, and translate business requirements into clear technical and functional designs.
• Collect, structure, cleanse, and harmonise data originating from multiple internal and external sources.
• Act as a technical lead on selected engagements, guiding delivery and technical decision-making.
• Design and implement scalable data architecture and ingestion frameworks to support efficient data processing, leveraging SQL, cloud-native services, and integration tooling across AWS and Azure environments.
• Build and maintain server-side components, including database layers, service integrations, APIs, and core application logic.
• Deploy, configure, and support platforms and tooling within a hybrid cloud setup.
• Prepare and structure datasets to support downstream analytics, reporting, and AI-driven use cases.
• Identify and implement improvements to strengthen data accuracy, consistency, and overall trustworthiness.
• Apply and uphold best practices around data governance, security, and lifecycle management.
• Support consulting and analytics teams by contributing to delivery plans, solution proposals, partner evaluations, and proof-of-concept activities.
• Provide guidance and coaching to less-experienced team members, supporting their technical development.

Key Experience Required:

• Previous hands-on experience delivering data engineering solutions or working in a closely related technical role.
• Strong practical expertise in Python and SQL, with the ability to apply both in production environments.
• Familiarity with workflow scheduling and orchestration platforms such as Apache Airflow or equivalent tools.
• Proven experience working across modern data platforms, including relational databases, data warehouses, data lakes, ingestion frameworks, and DataOps tooling.
• Demonstrated use of cloud-native services within both AWS and Microsoft Azure ecosystems.
• Solid back-end engineering capabilities, including an understanding of application servers, database layers, and API-based integrations.
• Working knowledge of common web frameworks and how they fit into full-stack architectures.
• Ability to investigate and diagnose data-related issues by performing root cause analysis across systems and processes, translating findings into actionable improvements.
• In-depth understanding of data modelling concepts and analytical techniques, including data mining approaches.
• Experience handling and processing semi-structured or unstructured data sources.
• Capability to design and implement pipelines that manage data transformations, schemas, metadata, dependencies, and workload scheduling.
• Strong command of source control practices using tools such as Git, alongside experience with automated build, test, and deployment pipelines.
• Hands-on experience supporting, maintaining, and evolving production data applications.
• Curiosity about emerging technologies and a proactive approach to expanding technical skill sets beyond core responsibilities.