Top Tech Jobs & Startup Jobs in London

Staff Software Engineer leading architecture and hands-on development across a scalable full-stack prediction markets platform. Responsibilities include building React, Next.js, TypeScript, and backend services; setting technical direction; leading cross-team initiatives; designing event-driven systems for transactions; improving reliability, observability, testing, and deployment; mentoring engineers; and establishing responsible AI-assisted development practices.

Analyze Sportsbook product performance through testing, reporting, ad hoc queries, and data-driven insights. Partner with product stakeholders, Analysts, and Data Scientists to optimize user experiences, track metrics, solve business problems, and support product development. Present findings to senior stakeholders and serve as a trusted source of analytical intelligence.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, and catalog registration; investigate and resolve pipeline issues; review code; trace data lineage; support data security, governance, and reverse ETL integrations; collaborate with stakeholders; and mentor junior data engineers.

Lead the design, development, testing, deployment, and maintenance of scalable promotions and rewards software. Guide engineering teams, make architecture and technical trade-off decisions, troubleshoot production issues, improve operational excellence, conduct code reviews, mentor engineers, and influence engineering best practices. Collaborate with product leaders and stakeholders while participating in planning, documentation, and on-call support. Occasional travel to offices, conferences, events, meetings, and team-building activities is required.

Lead platform engineering strategy and hands-on delivery across highly available cloud infrastructure, CI/CD, developer tooling, security, observability, and incident management. Establish standards for AI-assisted software development, validate AI-generated code, measure productivity and quality, and influence engineering, product, and quantitative teams. Mentor engineers and drive cross-team initiatives focused on scalability, resilience, compliance, and operational excellence.

Own end-to-end development of scalable full-stack experiences for a real-money prediction and trading platform. Design and implement React, Next.js, TypeScript, and backend services; build experimentation, observability, and deployment capabilities; conduct code reviews; collaborate cross-functionally; and establish effective AI-assisted development standards while helping shape an early-stage product.

Lead end-to-end development of high-performance, fault-tolerant backend services for a CFTC-regulated exchange. Responsibilities include modernizing Java systems, building order processing and market data services, managing trading data and reporting pipelines, ensuring observability and regulatory auditability, participating in 24/7 on-call, and applying AI coding tools while maintaining security, correctness, and reliability.

Leads platform engineering initiatives across AWS cloud infrastructure, Kubernetes, CI/CD, GitOps, Infrastructure as Code, observability, and developer tooling. The role architects reliable and scalable platforms, improves developer productivity and operational excellence, partners with application teams, leads cross-team technical programs, mentors engineers, and participates in on-call rotations.

Designs and maintains highly available cloud-native infrastructure, CI/CD pipelines, developer tooling, platform security, and observability systems. The role partners with engineering and product teams to deliver scalable solutions, leads incident response, evaluates AI-generated code for security and architectural risks, and mentors engineers. Candidates will use AI coding collaborators, infrastructure-as-code, Java and Python, and modern cloud platforms to improve developer productivity and operational reliability.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, catalog registration, reverse ETL, and governance controls. Investigate pipeline failures, perform lineage analysis, review code, identify downstream risks, and support data quality, security, and access controls. Collaborate with stakeholders, clarify requirements, document escalations, and mentor junior data engineers.