Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

Rebuild data infrastructure for a market-neutral systematic equities fund using Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, and GitHub Actions for CI/CD. Work on ETL pipelines, vendor integration, and cloud-based research platforms, with relevant experience in Snowflake, Bloomberg, MSCI Barra, or Databricks. The role spans data engineering and quantitative development to transform financial data into investment signals.

Build and own the machine learning models (propensity, uplift, contact strategy) that decide who, when, how often, and on which channel (voice, WhatsApp, email, SMS) Murphy's AI agents contact debtors to maximize recovery within legal limits. Work in Python, TypeScript, AWS, and PostgreSQL; design, run, and ship A/B tests and bandits on live traffic.

LiveScore Group★3.4 (14)· London

You will build and deploy robust data pipelines using GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Airflow, Terraform) and Python or SQL. As part of LiveScore Group's Data Team, you will develop scalable data systems for real-time AI/ML workloads that power sports media and betting for brands LiveScore, LiveScore Bet and Virgin Bet.

LiveScore Group★3.4 (14)· London

Use GCP (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore), SQL, Python, Terraform, Airflow, Docker and AI tooling. You will lead the data team in designing scalable pipelines, data models and real-time systems that power sports media and betting brands.

You will lead a small cross-functional data pod building data pipelines, models and services in Python, SQL and Snowflake to turn supply chain data into customer-facing sustainability insights. The role combines hands-on data engineering with technical leadership and line management of the pod's engineers.

You will work with GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore, Cloud Composer, Vertex AI), SQL, Python, Terraform, Airflow, and Docker. The data team builds scalable, real-time data pipelines and AI/ML workloads to power sports media and betting products for LiveScore, LiveScore Bet, and Virgin Bet. You will lead architectural decisions, own data-solution design, and mentor engineers.

You will work with GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore), Cloud Composer, Airflow, Docker, Terraform, Python, and SQL. The team builds scalable data pipelines and models to power real-time AI/ML workloads for LiveScore’s sports media and betting brands.

Databricks★3.0 (21)· London

You will work with Python, Scala, JavaScript/TypeScript, Apache Spark, and cloud ecosystems (AWS, Azure, GCP) to build production-grade data and AI solutions on the Databricks platform. In this hands-on role, you lead architecture and design decisions while embedding with customer teams to deliver end-to-end data pipelines, ML/AI models, and user-facing applications.

Fivetran★3.3 (8)· London

Senior Sales Engineer working with AWS, GCP, Azure, Snowflake, BigQuery, Databricks, Kubernetes, Docker, Podman, Grafana, Linux, Windows, Python, Java, and databases like Oracle, SQL Server, Postgres, MySQL, and NoSQL. The role supports Fivetran’s Commercial sales team by understanding customer data replication needs and leading proof-of-value processes for the company’s automated data pipeline and transformation solutions.

You will design and build data pipelines, models, and services in Python and SQL, using Snowflake for warehousing. You will lead a small cross-functional data pod that transforms supply chain data into customer-facing insights for Sedex’s SaaS sustainability platform. This role includes line management of the pod’s engineers.

**Python**, **SQL**, and **Snowflake** power the pipelines, models, and services this team builds to turn supply chain data into customer-facing insight. You will lead a small cross-functional data pod, combining hands-on engineering with line management of its engineers.

EPAM Systems★3.8 (703)· London

You will work with Python, SQL, GenAI frameworks (LLMs, RAG, LangChain, Hugging Face), cloud-native AI services (AWS, Azure, GCP), and MLOps tools such as Databricks or MLflow. As a consultant for EPAM, you will help clients shape and implement data-driven solutions, designing predictive models and GenAI-based applications to drive enterprise AI transformations.

You'll lead a 4–5 person team building machine learning systems using Python, TensorFlow, PyTorch, JAX, AWS, Docker, and Kubernetes for Super's Playstack platform, which powers sports betting and gaming experiences across Europe and Latin America. The role combines technical contribution, project management, and people leadership, focusing on predictive analytics, personalization, fraud detection, and real-time recommendations that enhance customer engagement in online gambling and sports betting.

You will build and maintain end-to-end data pipelines and orchestration workflows using GCP, BigQuery, Python, SQL, Cloud Run, Dataform, dbt, Fivetran, Docker, Git, and CI/CD within a growing Data & BI function. The team develops a modern cloud data estate and an intelligence platform that turns raw information into commercial insight, including supporting AI/LLM-enabled workflows for classification and summarisation.

Sainsbury’s★3.5 (16k)· London

In this Senior Data Engineer role you will design and build scalable data pipelines and platforms using PySpark or PyTorch on AWS EMR, Snowflake, Docker, Airflow, Kubernetes, SQL/NoSQL databases and CI/CD with GitHub Actions. You will partner with Data Scientists to make data reliable and production-ready for machine learning solutions, and mentor junior and mid-level engineers.

Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, GitHub Actions, Snowflake, Databricks, and OpenAI LLMs. The role rebuilds data infrastructure and transforms financial data into research-ready datasets and signals for a fully systematic market-neutral equity fund.

You will work hands-on daily with Python, SQL, Spark, Databricks/Delta Lake, orchestration tools (Dagster, Airflow, Temporal), dbt, Docker, and CI/CD. You will build and maintain data pipelines for the company's Insights platform, which turns raw financial data into advisory intelligence for commercial banking teams. As a senior engineer, you will set the technical direction for this platform.

Head of Quality Engineering to own quality across the organisation, working with PHP, Kotlin, GO, Python, Typescript, React, React Native, GraphQL, Relay, PHPUnit, Behat, JUnit, Kotest, Jest, Maestro, K6, Datadog, Sentry, Incident.io, Grafana, AWS, Docker, Kubernetes, RabbitMQ, Argo, Github, and Github Actions. The role will lead the use of AI (LLMs) for testing and will manage a team of approximately two Quality Engineers while being hands-on with code and CI pipelines. Lendable builds technology for consumer credit and savings products.

Manage the DMI config-driven data platform, working with dbt (macros, Jinja, incremental models), Python 3.11+, BigQuery, Google Cloud Platform (Cloud Functions, Cloud Run, Cloud Scheduler, Pub/Sub), Terraform, and CI/CD (Cloud Build). You will build ELT pipelines that ingest paid media data from 26+ ad platforms, transform it via dbt, and deliver analysis-ready tables. This role leads and mentors a team of data engineers.

You will work with dbt, SQL, Python, BigQuery, Cloud Functions, Cloud Run, and other GCP services on the DMI platform—a config-driven system that ingests, transforms, and delivers paid media data from 26+ ad platforms for global clients. The role involves building ELT pipelines, writing dbt models and Jinja templates, and maintaining infrastructure using Git, CI/CD, Docker, and Terraform.

UBS★3.8 (2.5k)· London

You will work with Python, Airflow, Prefect, Azure (AKS, EventHub, Terraform), Delta tables, DuckDB, Apache Spark, PostgreSQL, KDB, Docker, Kubernetes, JupyterHub, PowerBI, Streamlit, Marimo, and Java. You will lead the Credit Principal Algo Data team within UBS Global Markets, building and enhancing the trading analytics platform for automated eCredit trading.

Beamery★4.0 (1)· London

Beamery’s Data Engineering Team builds a self-service reporting and AI data platform, ingesting multiple sources into centralized infrastructure. The Senior Data Engineer leads ETL pipeline and analytics layer development, mentors engineers, and shapes the platform underpinning the company’s talent lifecycle management solutions.

You'll work with Google BigQuery, dbt Core, Airflow, Airbyte, GitHub Actions, ThoughtSpot, Colab/Jupyter and Python. Apolitical builds products for public-sector professionals. You'll model warehouse data, define metrics, ship dashboards and own the analytics stack end-to-end as an individual contributor.

You'll lead Python and FastAPI development on Lendable's Data Team, building data pipelines, APIs, and services deployed via Docker, Kubernetes, and AWS with ArgoCD. The fintech company rebuilds consumer finance products (loans, credit cards, car finance) to serve UK and US markets faster than traditional banks. You'll mentor a small team of 1–2 engineers while owning technical direction and remaining hands-on with code.