Top Data Engineer Jobs in London

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

Lead architecture and engineering for B2B agentic commerce and machine-learning systems in global payments. Define multi-agent architectures, authorization, information barriers, MLOps pipelines, evaluation frameworks, safety guardrails, and model-risk evidence. Develop and review secure production code, establish AI-assisted engineering standards, integrate external agents, improve operational resilience, and guide technical teams and vendors. The role requires advanced Python, production LLM agent experience, secure distributed-systems expertise, and financial-services technology knowledge.

Build, analyze, maintain, and optimize scalable data pipelines and complex data infrastructure. Develop ETL processes, data models, algorithms, and production software while working with large datasets and distributed batch processing. Collaborate with global engineering and product teams through analysis, prototyping, development, deployment, and maintenance. Apply data engineering, database, cloud, workflow orchestration, and software engineering practices in a dynamic agile environment.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

Leads hands-on development of secure, scalable cloud-native software, data engineering, backend systems, and AI/ML infrastructure. Designs microservices and distributed applications, builds production-scale data platforms, develops architecture artifacts, and promotes responsible AI-assisted engineering practices. Works with AWS data services, containers, Kubernetes, DevOps, and MLOps while supporting engineering standards, automation, troubleshooting, and stakeholder communication.

Design and build low-latency, real-time liquidity observability systems and streaming data pipelines for global financial operations. Architect Kafka and Flink pipelines, develop real-time aggregation and event-driven architectures, model streaming and analytical data, and ensure reliability through idempotency, exactly-once processing, failure recovery, and schema evolution. Collaborate with product, data science, quantitative analysis, and treasury teams to support liquidity positioning, forecasting, and operational visibility.

Develop and integrate statistical and machine learning sports models for DraftKings’ Sportsbook platform. Responsibilities include engineering sports data assets, implementing models and pricing engines in Python, testing data flows and integrations, supporting CI/CD quality practices, collaborating across Trading, Product, Engineering, and QA, and mentoring junior data scientists.

Build and maintain secure, scalable Azure data pipelines and ETL processes using Python and PySpark. Translate architecture designs into production implementations, curate healthcare and NHS datasets, conduct code reviews, document data mappings, and support governance, security, and compliance standards. Collaborate with researchers, engineers, and stakeholders to deliver reliable, research-ready data assets while challenging assumptions and improving technical designs.

Lead data architecture and engineering for Wise’s marketing platform. Build scalable streaming and batch pipelines, productionize customer data workflows using Airflow and dbt, improve reliability and data quality, and bring MarTech integrations in-house. Partner with engineering, analytics, product, and marketing teams to scope ambiguous problems and deliver robust solutions. Mentor software engineers on data engineering best practices, architecture, and implementation.

Build and govern canonical, semantic, and analytical data products for Private Banking and Wealth Management. Responsibilities include designing cross-domain data models, developing scalable data pipelines, implementing data quality checks and SLAs, documenting reusable data assets, and collaborating with architects, engineers, BI teams, data scientists, and business stakeholders on AWS cloud data platforms.

Lead development of production AI agents and machine learning workflows for B2B agentic commerce and payments. Responsibilities include agent orchestration, negotiation, onboarding, model training and serving on Kubernetes, evaluation, observability, secure software development, model-risk documentation, and technical leadership. The role also guides AI-assisted engineering practices and evaluates architectures with vendors and internal teams.

Lead Funding Circle’s data engineering and platform architecture across Snowflake and AWS. Own the future-state data platform strategy, migration roadmap, and scalable warehouse, lakehouse, streaming, and analytics solutions. Define data product standards, governance, quality, lineage, observability, and architecture decisions. Partner with Product, Engineering, Data Science, and Governance teams while mentoring engineers and enabling AI-focused data solutions.

Build and scale a centralized data platform for investment teams. Responsibilities include gathering requirements from portfolio managers and researchers, evaluating structured and unstructured financial datasets, developing reliable processing workflows, improving data infrastructure, and creating reusable tools for analysis and quality checks. The role requires Python programming, familiarity with ETL and data pipelines, relational databases or SQL, and a STEM degree.

Join the Data Strategy team to design, build, and maintain cloud-based ETL/ELT pipelines and data services using Python, SQL, Spark, and Databricks. Integrate heterogeneous data sources, prepare curated datasets, support production operations, and contribute to migration and platform modernization efforts for analytics and downstream consumers.

Build and maintain production-grade ELT pipelines and a performant BigQuery data warehouse using Dataform. Ingest structured and unstructured data from databases, cloud storage, APIs, and third-party feeds. Develop reporting datasets, data models, validation, monitoring, governance, documentation, and access controls. Collaborate with product, operations, AI, engineering, finance, and BI teams to translate business and financial requirements into reliable, trusted data infrastructure.

Support building and maintaining data pipelines, onboard and transform partner data, monitor reliability, validate data quality, automate processes, and document pipeline logic to ensure trustworthy datasets for the business.

Design and deliver scalable data solutions on Microsoft Azure. Collaborate with engineers, analysts, architects, and product teams; support data, telemetry, and operational understanding; develop proof of concepts; and build reliable, tested, maintainable solutions using CI/CD. Contribute to engineering standards, agile delivery, data modeling, performance and cost optimization, and adoption of emerging data technologies.

Designs and develops secure, scalable agentic commerce solutions for payments. Responsibilities include building agent orchestration components and MCP servers, developing Python services, creating Databricks data pipelines, supporting MLOps workflows, evaluating agent behavior, implementing observability, and improving operational stability. The role requires cloud-native engineering, API and event-driven services, LLM application development, responsible AI practices, Kubernetes, and secure production software delivery.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design and maintain scalable data platform infrastructure, ingestion pipelines, ETL/ELT workflows, data models, governance, reliability, and observability. Collaborate with analytics and engineering teams, support data warehouse architecture, participate in on-call incident response, document operational processes, and establish strong engineering practices for analytics and future AI capabilities.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and maintain data pipelines, models, ETL workflows, validation processes, and interfaces for defence modelling and simulation data. Transform complex simulation outputs into reliable datasets for experimentation, analysis, visualisation, and capability assessment. Collaborate with modelling and simulation engineers, software engineers, analysts, and domain specialists. The role requires Python, SQL, data-quality practices, and the ability to work in offline or restricted environments supporting UK Ministry of Defence projects.

Develop and maintain data pipelines, models, ETL workflows, and validation processes for defence modelling and simulation data. Integrate simulation and analytical environments, automate repeatable processing, and make complex datasets accessible for experimentation, analysis, and visualisation. The role requires Python, SQL, data modelling, and experience working in offline or restricted environments, supporting UK Ministry of Defence projects.

Leads development of secure, scalable data platforms supporting batch, streaming, ML, and LLM workloads. Designs distributed data systems using Spark, Ray, Kafka, Iceberg, and OpenSearch; productionizes model training, deployment, monitoring, and lifecycle management; and builds governed LLM solutions. Provides technical leadership, code review, incident response, vendor evaluation, responsible AI guidance, automation, and mentorship across engineering teams.

Designs and maintains secure, scalable data pipelines for defence, government, and national security customers. Responsibilities include ETL development, data profiling, modelling, quality controls, governance, anonymisation, cloud storage, workflow optimisation, and support for analytics and visualisation. The role also contributes to data engineering strategy, standards, research, and stakeholder presentations across the full project lifecycle.