Summary
✨ AI‑Generated
Build scalable data solutions that simplify complex organizational processes and create measurable business value. You will design data pipelines, modeling and ETL processes, automate manual stages, contribute to data architecture, and help teams adopt new data engineering techniques while maintaining strong security practices.
Highlights
Senior data engineering role focused on building scalable data architecture, advanced pipeline automation, reusable data solutions, and new data capabilities while balancing commercial value with data security.
Description
What you'll do
As a Data Engineer, you'll be looking to simplify our organisation by developing innovative data driven solutions through data pipelines, modelling and ETL design, inspiring to be commercially successful while keeping our customers, and the bank's data, safe and secure.
You'll drive customer value by understanding complex business problems and requirements to correctly apply the most appropriate and reusable tool to gather and build data solutions.
You'll support our strategic direction by engaging with the data engineering community to deliver opportunities, along with carrying out complex data engineering tasks to build a scalable data architecture.
Your responsibilities will also include:
Building advanced automation of data engineering pipelines through removal of manual stagesEmbedding new data techniques into our business through role modelling, training, and experiment design oversightDelivering a clear understanding of data platform costs to meet your department's cost saving and income targetsSourcing new data using the most appropriate tooling for the situationDeveloping solutions for streaming data ingestion and transformations in line with our streaming strategyThe skills you'll need
To thrive in this role, you'll need strong experience with cloud data warehouses, particularly Snowflake, strong Unix and Linux knowledge, and hands-on experience with data modelling and AWS Cloud services, including EC2, Linux, S3, Glue, Python, Spark, Airflow and Autosys.
Experience in banking and financial crime domains, including fraud, customer due diligence, KYC and AML, alongside the use of AI tools to enhance day-to-day efficiency, would be advantageous.
Additionally, you'll need:
Hands on experience of ETL technical products such as Powercenter, DEI, IDMC, SQL and PL-SQLExperience working in Agile teams and sprint planning, with hands-on experience defining user stories, tasks and RAIDs using JiraData warehousing and data modelling capabilitiesA good understanding of modern code development practicesExperience of working in a governed, and regulatory environmentExcellent communication, presentation and documentation skills, with the ability to influence senior stakeholders through clear, actionable recommendationsProficiency in Python for data processing, Automation and integration with GenAI models would also be beneficial
