AWS Data Technical Specialist - Glue, SQL, Python, DevOps, Snowflake

Job Summary

Data Engineer (Job Profile: Data Engineer C9)

Join us as a Data Engineer

• You'll be the voice of our customers, using data to tell their stories and put them at the heart of all decision-making
• We'll look to you to drive the build of effortless, digital first customer experiences
• If you're ready for a new challenge and want to make a far-reaching impact through your work, this could be the opportunity you're looking for

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.

Key Responsibilities

Your responsibilities will also include:

• Building advanced automation of data engineering pipelines through removal of manual stages
• Embedding new data techniques into our business through role modelling, training, and experiment design oversight
• Delivering a clear understanding of data platform costs to meet your department's cost saving and income targets
• Sourcing new data using the most appropriate tooling for the situation
• Developing solutions for streaming data ingestion and transformations in line with our streaming strategy

Skill Requirements

The 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-SQL
• Experience working in Agile teams and sprint planning, with hands-on experience defining user stories, tasks and RAIDs using Jira
• Data warehousing and data modelling capabilities
• A good understanding of modern code development practices
• Experience of working in a governed, and regulatory environment
• Excellent communication, presentation and documentation skills, with the ability to influence senior stakeholders through clear, actionable recommendations
• Proficiency in Python for data processing, Automation and integration with GenAI models would also be beneficial

Other Requirements