Work with Snowflake data architecture, SQL, Python pipelines, RESTful APIs, the Microsoft Power Platform, React front ends, and, optionally, ServiceNow, Workday, and Azure services. The Data and Applications team builds analytics, AI-powered tools, and data infrastructure for PJT Partners, a global advisory-focused investment bank.

Build backend databases in SQL Server and Azure, build machine-learning models in Azure, Microsoft Fabric and Alteryx, and develop front-end apps using Python, SQL, C#/.NET and JavaScript for the SMDAM Private Markets team. The role drives private credit data infrastructure end-to-end, extracting insights from text to power deal-sourcing and monitoring across the entire private credit lifecycle.

You will develop neuro-symbolic AI architectures for Project SENTINEL, an Innovate UK grant-funded initiative building provably safe guardrails for autonomous systems. Your daily work involves Python and ML libraries (PyTorch, TensorFlow, scikit-learn) to design deep learning models, NLP systems, and scalable ML pipelines for production deployment.

You will work with Python, SQL, Pandas, Snowflake, Elasticsearch, Docker, Kubernetes, Airflow, Spark, and Apache Kafka to build and maintain thousands of data ingestion pipelines and monitoring platforms. These systems feed investment decisions and model the fund’s view of the financial world.

You will use Azure Databricks, Azure Data Factory, Azure Data Lake Storage Gen2, and Azure SQL/PostgreSQL; develop in Python, Scala, and SQL; and work with Spark, CI/CD, Git, and Docker to build data pipelines. These solutions support HR and employee lifecycle processes within the company.

You'll work with AI agents, LLMs, test frameworks, CI tooling (GitHub Actions), and observability stacks (Datadog, Sentry, Grafana) to automate quality across Lendable's fintech credit products. The role enables cross-functional product teams to maintain high quality through hands-on coding, AI-assisted test authoring, and shift-left practices.

Senior Quality Engineers will build AI Agents, test frameworks, CI tooling and helpers using LLMs and AI tooling, working with GitHub Actions and observability (Datadog, Sentry, Grafana) across an AI-assisted testing function. The team defines Lendable's test pyramid, improves E2E automation, and partners with product and engineering to shift quality left across credit products.

Methods Business and Digital Technology★4.5 (2)· London

Design, build and improve ETL/ELT pipelines using SQL, Python, Apache Spark (PySpark/Spark SQL), Azure Data Factory or Airflow, Docker, and CI/CD. You will support client engagements to stabilise business-critical processes and establish repeatable data foundations across enterprise systems, working with data from Ariba, Workday, or SAP S/4HANA.

Build and maintain integrations between HubSpot, People Planner, and BigQuery using Python, SQL, REST APIs, and GCP tools (Cloud Run, Cloud Scheduler, Secret Manager, GitHub Actions). You'll own data flow for a media agency's operational systems, keeping data structured and synced into Notion for client teams.

T. Rowe Price★3.6 (709)· London

Work with SQL, Python, data warehouses, analytical platforms, UML, Jira, and Confluence to define and deliver strategic investment data capabilities for portfolio management, research, trading, analytics, and AI within the Investment Data Strategy & Architecture team at a global asset manager.

Work with Python, Nextflow/Snakemake, SMILES/SMARTS/SAFE, molecular fingerprints, LC-MS/MS data (mzML, MSConvert), and spectral databases (GNPS, MassBank, MoNA) to build schemas, QC pipelines, and datasets. You'll join Novogaia's small team creating foundation models that predict molecular structure from mass spectrometry data for natural-product drug discovery.

You'll work with Python, reproducible/containerized pipelines, cheminformatics representations (SMILES, InChIKey, fingerprints), and computational mass spectrometry. Novogaia is an applied AI drug discovery company building foundation models for molecular structure prediction from mass spectrometry data, and you'll own the design of internal benchmarks and evaluation pipelines for these models.

PlayStation★3.8 (164)· London

Design, build, and optimize data ingestion, transformation, and enrichment pipelines using SQL, Snowflake, Python, and REST APIs. You will join the Security Data Analytics team at PlayStation, developing data products and pipelines that help Information Security teams understand security posture and risk trends. This is an individual contributor role; no team size is stated.

Sumitomo Group★3.7 (463)· London

Work with SQL Server, Azure, Python, C#/.NET, JavaScript, Alteryx, Microsoft Fabric, PowerBI and DealCloud. Build backend databases, ML models and front‑end tools for the Private Markets team’s European LBO direct lending and private credit vehicles. The Data and Analytics team is adding its third key member to lead data‑platform engineering.

Python and C++ for developing scalable quantitative research frameworks, investigating data mining and machine learning algorithms, and modeling future price actions at a leading global quantitative trading firm that builds market prediction models.

Build and maintain ETL/ELT pipelines using Python, SQL, BigQuery, REST APIs, and GCP tooling (Cloud Run, Cloud Scheduler, Secret Manager) to sync data between HubSpot, a People Planner, and Notion. The role owns the flow of operational data (client, revenue, pipeline) into the data warehouse and reverse ETL back into business systems used by Finance, Ops, and Client Leadership teams.

Use SQL, Python, and AI tools to extract and analyze large data sets on private markets and alternative investments. Work with Product and Machine Learning teams to develop data extraction from unstructured text like PDFs. The company automates alternative investment data processes for institutional investors.

Work with Python, containerized pipelines, and cheminformatics representations (SMILES, InChIKey, molecular fingerprints) to build ML evaluation benchmarks for molecular structure prediction from mass spectrometry data. You will design adversarial checks, leakage-safe datasets, and repeatable pipelines that measure model quality for an applied AI drug discovery company.

Use Python, SQL, Databricks and Spark to develop predictive models, statistical analyses and A/B tests for Primark’s Data Science & Measurement team. You will build reusable, scalable data-science solutions that drive commercial decisions and business growth.

This role involves building data pipelines, Snowflake data models, and automation using SQL, Python, Java, or R, with practices like version control and CI. The engineer joins Sony Interactive Entertainment's Security Data Analytics team, supporting Information Security with analytics-ready data and AI-enabled workflows.

Work with Python, C++ (preferred), tree-based models, deep learning, and NLP to develop predictive features from HFT/intraday market data. As part of a multi-strategy hedge fund’s Equity/Futures team, you will design ML-driven alphas for cash equities and futures.

Expert C++ and Python developer with strong Linux skills will build data pipelines, prediction tools, and systematic trading infrastructure for an equities pod, using machine learning libraries and DevOps tooling (Google Cloud, Airflow, Influx DB, Grafana) to support large-scale quantitative research and intraday trading.

Work with Microsoft Fabric, Databricks, Azure, Snowflake, Python, Spark, SQL/NoSQL, PowerShell, Azure Data Factory, and Azure DevOps to build a Fabric Data Lakehouse for the Investment Management division. You will develop scalable ETL/ELT frameworks and medallion-architecture data pipelines as part of the team advancing USSIM’s data strategy.

Design, build and maintain scalable ETL data pipelines using Databricks, PySpark, Python and SQL for a leading global investment bank’s enterprise data platforms, supporting critical business initiatives.

You'll work with Python, SQL, AWS, cloud environments, database technologies, and source control to build scalable data solutions. The role supports a hedge fund's global trading and research environment, transforming raw data into trusted datasets for investment strategies and improving data platform reliability.