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# Credit Principal Algo Data Lead

- **Company:** UBS
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Data Analysis, Microsoft Azure, Continuous Integration, Data Infrastructure, Extract Transform Load (ETL), Relational Databases, PostgreSQL, Log Analysis, Online Transaction Processing, Operational Databases, Query Optimization, Power BI, Data Processing, ReactJS, Grafana, Apache Spark, Indexer, Data Analytics, Data Management, Streamlit Framework, Terraform, Data Pipelines, Docker
- **Published:** July 27, 2026
- **Apply:** https://jobs.ubs.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25008&siteid=5012&PageType=JobDetails&jobid=346471

## About the Role

* 8+ years of hands-on data engineering in production environments, with strong, idiomatic Python used for reliable, well-tested systems (not exploratory notebooks).
 * Design and operation of large-scale ETL/ELT pipelines, including orchestration (Airflow, Prefect, or similar) and reliability patterns such as retries, backfills, and exactly-once semantics.
 * Deep experience with Azure, including AKS, Helm, EventHub, observability tools (e.g. Log Analytics) and infrastructure-as-code (e.g. Terraform).
 * Advanced experience operating Delta tables at scale, covering transaction semantics, schema evolution, partitioning, and compaction, alongside highly optimised analytical query execution using DuckDB or Apache Spark.
 * Production-grade relational database expertise, particularly PostgreSQL (query tuning, indexing, partitioning, replication, and schema design across OLTP and analytics).
 * Strong KDB experience, covering key concepts such as tp/ctp/rdb/hdb and differences between real-time vs historical queries and usage of gateways.
 * Containerised data workloads using Docker and Kubernetes, with reproducible, environment-agnostic deployment of data infrastructure.
 * Analytical data modelling expertise, including dimensional models or data vaults, schema evolution, slowly changing dimensions, and downstream impact analysis.
 * Strong experience managing and troubleshooting environments based on visualization and data analytics technologies such as JupyterHub, PowerBI, Streamlit and Marimo.
 * Batch and streaming processing proficiency, including late data handling, watermarking, backfills, and explicit throughput vs. latency trade-offs.
 * Production data reliability and observability mindset, covering lineage, data quality checks, SLAs, CI/CD for data pipelines, and close collaboration with quants, risk managers, and traders to deliver trustworthy data products.
 * 2+ years of experience in Java, covering react-based applications.
 * Experience delivering business facing solutions enhanced by AI and using AI assisted development (including prompt driven workflows and MCP servers) to reduce context switching and accelerate reliable delivery.
 * You're curious to explore how AI can improve how we build, deliver, and optimize workflows. You do this with sound judgment - validating outputs and aligning with policies, risk standards, and ethical use.

*LI-GB

## Description

* Are you excited by the prospect of engineering solutions in Credit Principal Algo Trading team ? Are you passionate about building and delivering AI solutions?
 * We are seeking a talented and self-driven candidate to join and lead our Credit Principal Algo Credit Algo Data team within UBS Global Markets
 * This is a fast paced and collaborative team that are responsible for the development and enhancement of our growing trading analytics platform

We're looking for a Credit Principal Algo Data lead to:

 * Participate in the design, development, of our data platform
 * Play a key role in designing and implementing the next generation of platform, partnering closely with Algo Quant Trading, Trading, and Sales from idea generation through to production launch
 * Build and maintain strong working relationships with Algo Quant Trading, Trading, Quant, Sales, IT, and Risk partners in London and across regions


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