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.

Goldman Sachs★3.9 (2.3k)· London

Build a high-performance, low-latency data platform for post-trade processing using Java, Kafka, Flink, SingleStore, HDFS/S3, and Scala. You will own the full lifecycle of core systems, designing distributed computation and an AI-powered UI/agent framework to deliver intelligent analytics over large-scale business data.

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.

Amazon★3.5 (56k)· London

Develop generative AI and large models using Java, C++, Python, and Unix/Linux for Prime Video’s observability team, which combines log management, tracing, and AI-powered analytics to detect and diagnose issues at unprecedented speed.

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'll work with Trino, Iceberg, dbt, Airflow, Kafka, Python, SQL, Java or Scala to build and optimize data pipelines on a Lakehouse following Medallion Architecture (Bronze, Silver, Gold layers). You'll join Wise’s Data Onboarding Team, part of the Finance Squad that stewards the company’s financial truth by processing hundreds of millions of high-throughput financial events daily into audit-ready structures. The team abstracts product complexity into reusable, domain-agnostic stream-enrichment pipelines.

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.

Senior Software Engineer II building data pipelines on Trino, Iceberg, Kafka, dbt, Airflow, and Python/SQL for a Finance Squad. The team handles high-throughput financial event data within a Medallion Architecture, creating reusable stream-enrichment pipelines to support financial reporting and assurance at a global money-transfer company.

Wise★3.5 (42)· London

Work with Trino, Iceberg, Kafka, dbt, Airflow, Python, SQL and Java/Scala to build streaming data pipelines on a Lakehouse using Medallion Architecture (Bronze/Silver/Gold). You will own data extraction, cleaning and modeling for the Data Onboarding team, which bridges product events and audit-ready financial structures for Wise’s global cross-border money movement platform.

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.

APL Logistics★3.3 (685)· London

You will work with Microsoft Fabric, Azure, Power Platform, ETL/ELT pipelines, APIs, and relational databases. This Regional Applications Team role manages data requests for EMEA and Americas, delivering data warehouses, pipelines, and automations for supply chain business users.

Connected Places Catapult★3.0 (2)· London

You will apply statistical analysis, machine learning, AI, and generative AI (LLMs, agentic workflows) using Python, and work with R, Java, Terraform, and C++. As a Consultant Data Scientist, you deliver digital and data solutions to accelerate innovation for public bodies and infrastructure providers in transport and the built environment. You operate within a project-based team under guidance of a Head of Applied Data Science.

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 work with Java, Spring, and MongoDB to build micro-services using Gitlab and DevOps practices. Your backend engineering role supports Goldman Sachs’ financial platforms for trading, sales, and asset management.

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.

Goldman Sachs★3.9 (2.3k)· London

You will work on backend micro-service development using Java, Spring, and MongoDB. At Goldman Sachs, a global investment banking and securities firm, you’ll build scalable software, low-latency systems, and data-driven solutions for trading, sales, and asset management.

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.

Work with the Databricks Data Intelligence Platform, Spark, PySpark, Python, Java or Scala, and major public cloud platforms (AWS, GCP, Azure). As a pre-sales engineer, you will demonstrate how Databricks' unified data and AI platform helps customers solve complex data challenges, collaborating with Account Executives and Senior Solutions Architects.

Design, develop, and optimize data pipelines using SQL, Python, Scala, Java, or Spark, along with ETL/ELT tools like Azure Data Factory, AWS Glue, and Apache Airflow, on cloud platforms such as Azure, AWS, or GCP. You will work with databases (SQL Server, Snowflake, MongoDB), big data technologies (Spark, Kafka, Databricks), and orchestration tools to build scalable cloud-based data platforms supporting enterprise analytics and machine learning.