Top AWS Jobs in London

Lead the development and support of public cloud platforms focusing on AWS and Azure, ensuring secure, scalable solutions while managing resources and providing BAU support.

The Senior Data Engineer will design and implement data pipelines for fraud prevention, collaborate with teams, and ensure high quality code while participating in agile methodologies.

Transform procurement, cloud and infrastructure data into dashboards, forecasts and insights to support capacity planning, supplier management, cost optimisation and investment decisions. Build ETL pipelines, KPI frameworks and automated reporting, partnering with procurement, finance, engineering and operations and presenting findings to senior stakeholders.

Build and manage multiple datasets, create automated ETL pipelines, integrate datasets, and ensure data quality for QRT's research and trading platform.

As a Front Office Quantitative Developer, you will enhance quant technology for algorithmic trading, collaborating with traders and quants to support 24/7 strategies.

Develop and enhance core technology for systematic trading, including front-office systems and automation of trading strategies. Collaborate with traders and quants.

Lead EMEA paid Priority Support end-to-end: own regional revenue targets, scale and coach the Designated Support Engineer team, drive proactive AI-augmented advisory engagements, partner with Sales and reactive support for escalations, and ensure customer retention, consumption growth, and delivery quality.

As a Senior DevOps Engineer at Scribe, you will design and optimize AWS cloud infrastructure, manage CI/CD pipelines, and support product development while ensuring reliability and performance of services.

Sell Databricks Lakebase (managed Postgres) to UKI customers by driving revenue, running multi-threaded sales cycles, orchestrating POCs, engaging platform and application teams, and enabling field teams to adopt Lakebase for operational and AI-native applications.

Lead technical pre-sales engagements for Databricks, demoing Spark and data/AI solutions, building architectures, authoring reference materials, and collaborating with account teams to design and deliver customised proofs-of-concept and workshops.

Design, build, test, and deploy scalable production platform infrastructure. Own CI/CD, observability, and reliability practices; collaborate cross-functionally, participate in incident management, create documentation, and drive platform engineering standards and automation.

Sell Databricks Data Intelligence Platform into large financial services accounts. Develop industry-specific use cases, co-develop business plans with partners, drive adoption of usage-based subscriptions, manage executive relationships, lead cross-functional teams, and close complex enterprise deals to exceed bookings and usage targets.

The MS Cloud Infrastructure Engineer manages cloud infrastructure deployment, supports operations, coaches junior engineers, and evaluates hardware/software solutions.

Own platform stability and roadmap as a commercially accountable Technical Product Manager embedded in engineering. Prioritise platform investments by business value, track SLA/SLO and reliability metrics, act as single stakeholder interface between pods and leadership, enforce CI/CD standards, and contribute technically by writing code, reviewing PRs, and participating in architecture design.

Build and maintain low-latency, high-throughput backend services and data pipelines; own service reliability, observability, incident response, and automation; operate streaming and data infrastructure; extend IaC and CI/CD; reduce toil and improve production resilience.

Build, operate and improve the internal platform, tooling and automation that development teams use. Manage infrastructure as code (AWS CDK), extend CI/CD pipelines (CircleCI -> GitHub Actions), support runtimes (Lambda, ECS, EKS), operate data/streaming systems, improve observability in Datadog, and support incident response and developer adoption.

Senior individual-contributor role owning architecture for the companys most complex systems. Lead design and production of full-stack, AI-native platforms (LLM workflows, intelligent pipelines), set engineering standards, mentor engineers, and translate ambiguous product bets into concrete technical plans while preventing systemic technical risk.

Lead complex digital forensic and investigative engagements for clients, advising on regulatory and legal matters, conducting device/cloud forensics, directing mobile extractions and analyses, developing cloud forensics methodologies, integrating automation and AI techniques, acting as expert witness, mentoring junior staff, and collaborating across EMEA and global teams. Travel to client sites in the UK and internationally as required.

Design, develop, and deploy scalable ML and time-series forecasting engines integrated into Anaplan applications. Lead customer onboarding and customize models to client data, build feature pipelines (SQL/Python/dbt), and operationalize models via MLOps tools (MLflow, Optuna). Collaborate with Product, Engineering, and Customer Success to deliver production-grade, monitored solutions.

Lead data analysis and modeling efforts, managing large datasets, and translating insights into actionable business knowledge while providing analytical thought leadership.

Lead content strategy and execution for eCommerce clients: develop content audits, calendars, tone of voice, and discoverability strategies; manage a multidisciplinary content team, define KPIs and measurement frameworks, optimize for SEO/GEO and conversion, and establish ethical AI and accessibility governance across digital experiences.

This role involves leading data analysis, managing datasets, developing predictive modeling solutions, and providing analytic thought leadership for business problems.

Design, build, and operate scalable web applications, APIs, services, and AI-enabled workflows. Use AI tools across the development lifecycle, ensure reliability, performance, and observability, contribute to technical decisions, iterate from user feedback, and own production quality and delivery as part of cross-functional squads.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Own QA for web and mobile releases: write and run manual and automated tests (web, mobile, API), build test frameworks, improve CI, track issues, mentor teammates, and influence requirements and quality processes.