> Markdown version of [/jobs/ext/4612721-senior-aws-data-engineer](https://www.wearedevelopers.com/jobs/ext/4612721-senior-aws-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AWS Data Engineer - **Company:** Microlise - **Location:** London, United Kingdom - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Data as a Services, Data Architecture, Information Engineering, Data Governance, Software Design Patterns, Python (Programming Language), Machine Learning, Metadata, Operational Data Store, Data Mesh, SQL Databases, Data Streaming, Data Processing, AI Platforms, AWS Data Analytics, Operational Systems, Data Management, Api Design - **Published:** November 7, 2026 - **Apply:** https://www.apply4u.co.uk/jobs/senior-aws-data-engineer/55320820 ## About the Role Bring the skills to turn data into trusted products Able to act as a mentor and coach to others within the team. Someone who can bring a passion for data, SQL, dbt and Python, with experience building scalable pipelines, transformations and reusable datasets Engages data engineering delivery with curiosity and ownership. Someone who builds in data contracts, metadata, lineage, and data quality as part of the data flow process. Experience with AI-ready data products would be highly valuable. Good understanding of lakehouse architecture, medallion patterns, data modelling, data quality, and data governance. ## Description Join Microlise as a Senior AWS Data Engineer on a 12-month fixed term contract and help build the data foundations behind the next generation of customer-facing analytics and AI products. This is a hands-on engineering role in a major cloud data platform programme, where you'll help turn operational, telematics, customer and third-party data into trusted, reusable data products that power dashboards, APIs, self-service analytics, machine learning and future generative AI services. You'll work with modern AWS managed services, lakehouse architecture, Data Mesh principles and governance-by-design patterns to create data that is reliable, discoverable, secure and ready for real customer impact. If you enjoy building robust data platforms, solving complex integration problems and shaping how data enables better products, this is an opportunity to do meaningful engineering work that will be reused across the Microlise product portfolio. What You'll Be Doing Make a visible impact on customer-facing analytics and AI Microlise is investing in an Analytics and AI platform that connects data across multiple complex operational systems and third-party sources. Your work will help make that data trusted, governed and easy to reuse, enabling faster analytics delivery, richer customer insights, API-driven data services and future AI capabilities. The pipelines, models and governance patterns you build won't sit in the background. They will directly influence how data is trusted, shared and turned into value for customers and internal teams. Start by building something customers will use Your first focus will be working on a greenfield AWS Analytics and AI platform Establishing reusable engineering patterns for future products. Build AWS lakehouse pipelines using services such as S3, Glue, Athena, Lake Formation, DMS and Step Functions Transform operational data into governed, reusable products for dashboards, APIs and customer-facing analytics Build in data quality, lineage, metadata, and access controls from the start Build, shape and scale a modern AWS data platform You'll be part of the team turning platform strategy into working, production-ready data capabilities Design, build and optimise cloud-native data pipelines, lakehouse structures and reusable data products on AWS Use lakehouse and medallion architecture concepts to move data from raw ingestion through to curated, business-ready products Create resilient data transformations that support reliable customer-facing analytics Embed governance by design, including metadata, lineage, data contracts, access controls and tenant-aware data handling Support product-level observability, performance optimisation, cost awareness and operational reliability