Top Data Engineer Jobs in London

Own AI safety datasets from collection through labeling, quality control, versioning, and training readiness. Translate safety policies into consistent labeling and evaluation criteria, manage external contributors, and build production model evaluation workflows. Develop lightweight Python and SQL pipelines for reproducible data analysis, partnering with safety researchers, policy specialists, and ML engineers. The role also contributes to scalable dataset methodologies, quality standards, and human-in-the-loop processes.

Build and operate reliable production data pipelines, ingestion systems, orchestration, customer data connectors, and infrastructure as code. Manage AWS-based data infrastructure using Terraform, Fivetran, AWS DMS, dbt, PostgreSQL, Lambda, and related services. Develop secure, enterprise-grade integrations for HR and payroll systems, and help connect trusted data to AI agents through MCP connectors and semantic data layers. Collaborate with product stakeholders to design scalable solutions meeting enterprise security and reliability standards.

Lead the technical direction and architecture for Arbor’s data platform, spanning transactional, analytical, and AI systems. Define target architectures, guide technical leads, support product delivery, and enable AI-first engineering through LLM orchestration, RAG, and real-time inference infrastructure. The role includes mentoring, architectural reviews, data governance, security, compliance, observability, DataOps, cloud platform cost optimization, and cross-functional collaboration with product and commercial leaders.

Own the end-to-end design, reliability, resilience, and recovery of production data ingestion systems and pipelines. Build batch and streaming ETL/ELT workflows, establish data quality checks and SLAs, improve monitoring and alerting, resolve platform issues, reduce technical debt, and optimize performance and cost. Collaborate with stakeholders, AI teams, and international expansion initiatives while supporting time-series, textual, and event-stream data workloads.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Operate and architect large-scale distributed data infrastructure across Kafka, Hadoop HDFS, Ceph, SQL Server, bare-metal systems, and hybrid cloud environments. Responsibilities include capacity planning, reliability engineering, automation, observability, incident response, backup and recovery, platform tooling, and architecture decisions. The role requires expertise with petabyte-scale systems or billions of daily events, mentoring engineers, and shaping infrastructure standards across engineering organizations.

Design, develop, and maintain data pipelines using Kafka; build infrastructure with Terraform; troubleshoot data engineering issues and collaborate with teams.

Build and maintain production-grade data pipelines, design data models and infrastructure, enable API access, govern data assets, and collaborate with actuaries, data scientists, and engineers to support algorithmic underwriting and MLOps.

Design and build cloud-based data pipelines using AWS tools, collaborate with clients, ensure data governance, and mentor junior engineers.

Designs and implements complex Cisco data centre and networking solutions for enterprise customers. Responsibilities include deploying Nexus, VXLAN, and ACI environments; integrating security and network service appliances; analyzing performance; resolving complex technical issues; automating deployments with AI and infrastructure-as-code practices; developing reusable delivery frameworks; and influencing product roadmaps. The role is customer-facing and requires deep networking expertise, consulting or service delivery experience, and professional-level certifications.

Lead the design and implementation of modern data platforms and pipelines, ensuring high performance and compliance while consulting directly with clients to drive effective data solutions.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

As a Data Engineer, you'll optimize the Data Lake and develop pipelines for data-driven decision-making, collaborating with Data Science for efficiency.

Build and maintain reliable ingestion and transformation pipelines on Databricks or Snowflake, design Data Vault 2.0/Kimball models, ensure production reliability, support testing/monitoring/documentation, and help apply LLMs and AI-enabled solutions in regulated financial services environments.

Lead technically informed sourcing and procurement for electronic components and EMS/CM partners. Own RFQs, cost modeling, supplier negotiations, qualification, NPI sourcing, BOM cost analysis, lifecycle management, risk mitigation, and dual-sourcing strategies. Collaborate with engineering, manufacturing, quality, finance, and program teams to optimize cost, availability, quality, and scalability across the product lifecycle.

Build and operate globally distributed data-transfer infrastructure for video, LiDAR, and sensor data at petabyte-per-day scale. Design parallelized pipelines using Flyte, Kafka, Azure, Python, and Terraform; implement reliability, observability, integrity checks, backfills, dashboards, alerting, and cloud-to-cloud networking. Collaborate with product and external partner teams, support evolving requirements, and participate in an out-of-hours on-call rotation.

Design, build, deploy, and optimize scalable data pipelines and ETL/ELT processes using Azure Databricks and related Azure services. Develop data models, data quality frameworks, integration solutions, and technical documentation for insurance data transformation programs. Collaborate with stakeholders, lead technical delivery, and apply data governance, warehousing, distributed computing, Agile, and DevOps practices.

Build and optimise scalable, serverless data pipelines and storage on GCP. Implement ETL, CI/CD and DataOps practices, support model productionisation with data scientists, document architectures, and guide migration toward event-driven managed cloud services.

Lead architectural and technical decisions for shared data-platform capabilities, combining hands-on software engineering with Staff-level technical leadership. Build scalable cloud-native systems and data pipelines, mentor engineers, collaborate with product and cross-functional stakeholders, integrate partner systems, and support live operations and incident response. The role focuses on reliable processing of large volumes of sensitive health and research data while improving engineering practices across multiple teams.

Build and operate a petabyte-scale data platform and Capability Factory: ingest, curate, preprocess, and version robot telemetry; design scalable batch/stream pipelines, orchestration, compute and storage; collaborate with ML teams on MLOps and evaluation workflows; build data warehouse and BI dashboards; ensure data quality, access control, and production ownership including on-call incident triage.

Build and maintain device software and data pipelines for handheld gripper data collection devices. Implement sensor integration and temporal synchronization, recording/packaging episodes, validation tooling to detect data faults, operator-facing controls, and on-site support. Debug hardware integration issues and collaborate with hardware, AI, and data teams to ensure high-quality training datasets.

Design, implement, and maintain data lakehouse architectures on AWS; build secure data pipelines with PII protections and compliance; advise clients across technical and executive stakeholders; manage data residency requirements; collaborate with analytics, security, and legal teams; support ML feature stores and model deployment.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Staff Backend Engineer will lead the technical vision for Depop's data platform, focusing on data ingestion, scalability, observability, and architecting data solutions while mentoring team members.