About Us
Our client operates across transportation, freight and logistics, while developing software and telecommunications capabilities that support connected services, digital workflows and data-intensive operations. Its work brings together operational technology, software platforms, mobile applications and information management to improve how services are delivered, coordinated and experienced. A broad technology environment creates a strong setting for engineers who want to build dependable data foundations with practical commercial impact.
Job Description
The AWS Data Engineer will design and deliver the cloud data capabilities that enable reliable reporting, advanced analytics and future digital products. Working from Nottingham, you will engineer ingestion, transformation and storage services across a modern AWS estate, ensuring that data can move efficiently from operational and external sources into trusted, well-structured datasets.
Success in this position means producing resilient pipelines, clear data models and reusable data products that meet business and technical needs. You will combine hands-on engineering with sound architectural judgement, strengthening quality, governance, observability and cost control while working closely with platform, architecture, product and analytical specialists.
The role offers substantial scope to influence engineering standards and the evolution of a scalable data platform. Your contribution will help establish consistent delivery patterns for batch, event-driven and change-data-capture workloads, supporting both current priorities and the organisation’s longer-term data and AI ambitions.
Key Responsibilities
- Architect and implement secure, scalable data pipelines using AWS-native services and modern engineering patterns.
- Develop ingestion frameworks for relational systems, APIs, files, events and change-data-capture sources.
- Build transformation workflows and dimensional or domain-oriented models using SQL, Python and dbt.
- Structure data across lakehouse layers so that raw, curated and consumption-ready information remains traceable and fit for purpose.
- Create reusable data products that serve operational insight, customer solutions, reporting, machine learning and emerging AI use cases.
- Establish automated controls for validation, reconciliation, completeness, freshness and other critical data-quality measures.
- Embed metadata, lineage, access management and governance requirements into the engineering lifecycle.
- Use monitoring, logging and alerting to improve pipeline reliability, incident response and platform observability.
- Review workload performance and cloud consumption, identifying opportunities to improve efficiency and manage AWS costs responsibly.
- Partner with architects, software engineers, analysts, product owners and platform teams to translate requirements into maintainable solutions.
- Contribute to engineering standards, reusable components, documentation, code reviews and continuous improvement practices.
- Help shape data-product ownership and federated delivery principles as the wider platform matures.
Requirements
- Demonstrable experience designing and operating production data engineering solutions on Amazon Web Services.
- Strong practical knowledge of Amazon S3, AWS Glue, Athena, Lake Formation, AWS Database Migration Service and Step Functions.
- Advanced SQL capability, including complex transformations, performance considerations and data validation techniques.
- Professional Python experience with a focus on automation, pipeline development, testing and maintainable code.
- Hands-on delivery experience with dbt, including modular transformations, testing, documentation and model dependency management.
- Understanding of lakehouse architecture, layered data design and principles such as the Medallion approach.
- Experience integrating data through batch processing, APIs, file exchange, streaming or event-based mechanisms.
- Knowledge of data governance disciplines covering quality, metadata, lineage, security and appropriate access controls.
- Ability to design for resilience, observability, recoverability and efficient use of cloud resources.
- Experience communicating technical decisions clearly to both engineering colleagues and non-technical stakeholders.
- Confidence working across multidisciplinary delivery teams in a complex or enterprise-scale environment.
- Exposure to customer-facing analytics, operational intelligence or data-enabled digital products would be advantageous.
- Familiarity with machine learning, artificial intelligence or advanced analytical workloads would add value.
- Awareness of Data Mesh, domain ownership or data-product operating models would be beneficial.
- AWS certification, such as Solutions Architect, Data Engineer or Developer, would be welcomed but is not essential.
Benefits
- Meaningful ownership of cloud data engineering work that supports transportation, logistics and digitally enabled services.
- Technical scope across ingestion, lakehouse design, governance, data products and AWS platform optimisation rather than a narrowly defined pipeline remit.
- Exposure to analytics, machine learning and emerging AI initiatives as the organisation expands its data capabilities.
- Opportunity to influence reusable engineering standards, delivery patterns and the practical adoption of data-product principles.
- Collaboration with architecture, platform, software, product and analytical professionals across a varied technology landscape.
- Access to a role where strong engineering decisions can improve reliability, insight and operational performance at scale.
- Professional growth through hands-on work with modern AWS services, contemporary data practices and complex integration challenges.
- A competitive package aligned with the responsibilities and experience required for this position.
Other
The position is based in Nottingham, England, and is suited to an engineer who combines strong AWS delivery skills with an interest in platform design and business outcomes. Experience from transportation, logistics, software, telecommunications or another data-intensive sector will transfer well.
Applicants should be prepared to demonstrate how they have improved the reliability, usability or governance of production data platforms. The successful candidate will bring curiosity, practical judgement and the ability to turn evolving requirements into robust engineering outcomes.
Callum Bell
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