Top Data Jobs in London

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.

Own ETF data quality and accuracy across VettaFi’s consumer platforms. Audit and improve LLM-generated classifications, descriptions, and data extractions; build and maintain a unified ETF taxonomy and ontology; manage market data vendors; collaborate with Product, Engineering, and Marketing; and establish KPIs for data quality, completeness, timeliness, and platform performance.

Leads Carat Global’s digital, data, and technology vision while managing strategic client partnerships. Drives revenue growth, digital transformation, innovation, thought leadership, new-business pitches, platform relationships, and integrated media solutions. Partners with senior client, strategy, data, technology, and growth leaders to strengthen capabilities, develop digital roadmaps, advise clients, and improve global digital output. Also coaches talent, supports training, and ensures resources and skills align with client and business demands.

Serves as Jigsaw’s embedded data privacy advisor, guiding product, engineering, legal, and customer-facing teams on privacy compliance and commercial risk. Advises on UK, EU, and US privacy laws, cross-border data transfers, customer contracts, DPAs, AI-related questions, and privacy by design. Participates in customer calls, translates legal and technical concepts for varied audiences, and improves privacy templates, playbooks, and processes. This fractional role requires pragmatic judgment, strong communication, and flexibility to address urgent incidents.

Leads Carat Global’s digital, data, and technology vision while managing strategic global client accounts. The role drives profitable growth, digital innovation, thought leadership, new business pitches, client transformation roadmaps, platform partnerships, and integration across media, data, and technology teams. It also develops talent, strengthens digital capabilities, oversees client solutions, and raises the agency’s industry profile.

As a Senior Azure Data Engineer, you'll design and deploy data engineering pipelines using Databricks on Azure, collaborate with clients, and implement best practices in a consulting environment.

Lead architecture and delivery of data solutions on Databricks within Azure, ensuring best practices in data pipeline design and implementation.

The role involves designing and building data pipelines using AWS technologies, leading projects, and mentoring junior engineers in a data engineering context for financial services.

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 end-to-end data science and machine learning solutions for dynamic content targeting, from business conception through production deployment and impact evaluation. Partner with stakeholders, improve model quality and lifecycle processes, use model outputs to guide user experiences, and explore emerging AI techniques. The role requires advanced Python, SQL, dbt, statistical modeling, production ML deployment, and clear communication with technical and non-technical audiences.

Leads claims data analytics for UK Claims Operations, developing KPI dashboards, self-service reporting tools, statistical analyses, and management information. Provides insights to improve productivity, customer experience, claims outcomes, and operational efficiency. Ensures regulatory and internal policy compliance, supports data literacy, advises colleagues, and collaborates with stakeholders across the organization.

Build, optimize, and maintain secure batch and streaming data pipelines using Snowflake and cloud platforms. Develop data models, support ingestion and transformation, apply governance and security practices, and collaborate with architects, engineers, analysts, and clients. The role also involves performance tuning, data solution delivery, technical leadership, mentoring engineers, and contributing reusable assets and best practices to the data engineering practice.

Designs and delivers scalable modern big data platforms, including streaming, data lake, and lakehouse architectures. Advises senior client stakeholders on data strategies, cloud-native modernization, governance, analytics, and AI integration. Leads solution design, implementation, transformation initiatives, and technical collaboration across multiple sectors. Contributes thought leadership, reusable assets, and practice growth while aligning data platforms with measurable business outcomes.

Designs and delivers scalable batch and streaming data pipelines using Databricks and Lakehouse architectures. Responsibilities include ETL/ELT development, data modeling, Delta Lake optimization, governance, security, orchestration, and cloud integration. The role is client-facing and involves collaborating with architects, engineers, ML teams, analysts, and senior stakeholders. It also requires mentoring engineers, improving data practices, enabling analytics and AI use cases, and contributing reusable assets and technical leadership.

Designs and implements scalable, secure Snowflake cloud data platforms for enterprise clients. Leads data modernization, migration, architecture strategy, ETL/ELT implementation, governance, security, performance tuning, and analytics enablement. Acts as a trusted advisor to senior stakeholders, connects business objectives with technology execution, guides delivery teams, and contributes to consulting practice growth through thought leadership, reusable assets, and propositions.

Lead technical direction for complex data science and machine learning projects, design production-grade solutions, define roadmaps, manage technical risk, support sales engagements, advise customers, establish best practices, and mentor data scientists and cross-functional teams.

Lead AI safety projects for frontier labs, developing evaluation frameworks, scientific experiments, and supporting software architectures. Mentor technical teams, shape project scope and feasibility with commercial partners, advance AI evaluation research, and ensure rigorous scientific and technical standards throughout delivery.

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.

Leads a team responsible for the reliability, performance, and availability of GPU-accelerated HPC infrastructure. Oversees monitoring, incident and problem management, root cause analysis, automation, documentation, change management, and operational metrics. Owns Agile delivery practices, backlog alignment, cross-functional coordination, and continuous improvement. Acts as an escalation point during major incidents while managing team development, resource allocation, stakeholder communication, and third-party support relationships.

Join a hedge fund Compliance Control Room to manage restriction lists, monitor position limits, support MNPI and information barrier controls, field business queries, document and refine controls, gather requirements, and lead data and process improvement initiatives alongside technology teams.

Design, build and deploy enterprise-scale ML and AI solutions (including LLMs and RAG) to improve operational efficiency, service resilience and customer outcomes. Perform statistical modelling, feature engineering, experimentation, and large-scale data analysis. Move models from prototype to production, support monitoring/governance, and partner with engineering, SRE, product and business stakeholders to define metrics and deliver measurable business value.

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.