Data Engineering Jobs

Palantir Technologies · New York, United States · US$135,000 – US$200,000 pa

Builds and evolves APIs powered by Kubernetes controllers, bridging raw clusters and an infrastructure-agnostic runtime for hundreds of microservices. Works with forward deployed and customer engineers on a PaaS for US Government customers, shaping the product's early strategy and long-term tech roadmap.

Palantir Technologies · New York, United States · US$110,000 – US$147,000 pa

Ensures stability and reliability of mission-critical workflows built on Palantir software, going on call to resolve incidents before customers are impacted. Uses field learnings to drive product change, shape internal tooling, and automate or streamline workflows, documenting best practices to raise reliability across the organisation.

eFinancialCareers · London

First-line support for a high frequency trading environment, acting as the point of contact for traders, researchers and internal users. The role blends hands-on engineering with operations: resolving user issues, addressing data quality concerns, managing vendor relationships and alerts, integrating new datasets, configuring quality controls and keeping pipelines and APIs robust, scalable and production-ready. Sits within a London-based Global Data Engineering team supporting trading, with a strong Python and data modelling focus.

eFinancialCareers · London

Lead and mentor a small team of data engineers building the architecture of a proprietary trading firm's data platform, splitting time evenly between hands-on software development and leadership. The team owns data platform capabilities, managed infrastructure and metadata management systems underpinning a common query layer, working with Airflow, Kafka, Spark and Python or Java. The role combines people leadership, technical leadership, product ownership and stakeholder coordination.

Palantir Technologies · New York, United States · US$135,000 – US$200,000 pa

Works directly with customers to own Gen AI strategy and implementation, building end-to-end LLM workflows and taking them to production at large scale. Operates like a hands-on AI startup CTO in small teams, delivering high-stakes projects and feeding field learnings back into the AIP product suite. Requires strong engineering and ML foundations plus up to 25% travel to client sites.

Palantir Technologies · London

Works deep inside the data processing infrastructure behind Palantir Foundry, designing query planning and optimization, extending execution engines, and improving operators, parallelism and memory management. Builds on and extends Apache Spark, DataFusion, Comet, Velox and Iceberg, owning changes from design through production with a high bar for correctness and predictable performance.

eFinancialCareers · London

Joining a three-person storage team, you will design, deliver and support all storage and backup systems, ensuring 24x365 availability and capacity. The role uses DevOps and infrastructure-as-code practices with Python, PowerShell, Terraform or Ansible, plus enterprise products such as Pure Storage, Rubrik, VAST and Azure, working closely with server, database and research teams.

eFinancialCareers · London

Embedded within a hedge fund's investment team, building Python research platforms, simulation and backtesting frameworks, data pipelines and tooling for systematic trading strategies. Works daily with quantitative researchers and portfolio managers to productionise signals and models, with a focus on testing, CI/CD and scalable system design.

eFinancialCareers · London · £1,100 – £1,200 pd

Hands-on KDB+/q engineering role in a front-office technology team supporting electronic trading across Fixed Income markets. You will build real-time pricing, trading and risk applications, maintain data pipelines and APIs, and work directly with traders and quants on high-performance time-series platforms, including production support and releases.

eFinancialCareers · London

Partners with portfolio managers, IT and data engineering to assess data needs across equities, FX, rates, fixed income and credit, then sources and evaluates market, reference, pricing and alternative datasets for systematic strategies. Leads complex data projects, compares vendors and negotiates agreements, and advises on data operations such as cost allocation, cataloguing and delivery infrastructure.

eFinancialCareers · London

Lead a data and analytics organisation spanning strategy, governance, business intelligence, data science and experimentation. Owns game economy standards, monetization optimisation, segmentation and pricing, working with Product and Live Operations to grow player lifetime value. Also directs forecasting, machine learning and responsible AI use across the portfolio.

eFinancialCareers · London

Designs, builds and optimises a petabyte-scale data platform and Data Lake using cloud-native AWS tooling such as S3, EKS, Glue, Airflow, Trino, Parquet and Redshift, with Apache Iceberg adoption planned. The role sits in a flat-structured Data Services team supporting trading users, combining data engineering, DevOps and CI/CD automation on Linux/Unix infrastructure.

eFinancialCareers · London

Owns a greenfield Snowflake-based enterprise data platform for a private markets investment firm, covering architecture, ingestion pipelines and governance. Integrates portfolio, fund administration and market data into governed datasets, with hands-on Python, SQL and Azure work alongside senior stakeholders.

eFinancialCareers · London · £70,000 – £106,000 pa

Designs and builds a greenfield data platform in a hybrid Azure and on-prem environment, creating pipelines, ETL processes and data models while migrating legacy capability. Works within a circa 100-person EMEA Data & Analytics Technology team, mentoring peers and aligning delivery to platform strategy. Requires at least two days a week in the London office.

eFinancialCareers · London · £700 pd

Hands-on role building AI-powered applications, intelligent workflows and machine learning solutions in Python, taking them from concept through to enterprise production deployment. Involves LLMs, agentic frameworks, AI orchestration, model evaluation, testing and monitoring, working with technical teams and business stakeholders to drive AI adoption.