Data Engineer - ML Platform

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person.

Our Data Environment

Our Data teams are aligned to divisions covering the following Banking Services & Products, Customer Identity & Financial Crime and Data & ML Engineering. Our Data teams are excited about delivering meaningful and impactful insights to both the business and more importantly our customers. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.

Responsibilities:

• Analyse source data from source databases (PostgreSQL) to understand structures, relationships and semantics.
• Design and implement streaming data pipelines using AWS EMR and PySpark to generate real-time (fast-moving) features for the feature store.
• Develop and maintain batch processing pipelines using DBT and BigQuery to generate batch (slow-moving) features, ensuring data quality, consistency and reliability.
• Work with Feast feature store, manage feature life cycle and maintain data quality.
• Collaborate with data scientists, ML engineers and software engineers to identify requirements, define feature schemas and ensure efficient integration into Feast feature store.

• Good knowledge of programming languages such as Python or Java.

• Strong experience with streaming technologies (Spark, PySpark, Flink, KSQL or similar) for developing data transformation pipelines.
• Solid understanding and practical experience with SQL and relational databases (PostgreSQL preferred).
• Proficiency with AWS EMR for running and managing Spark workloads.
• Experience in SQL-based transformation workflows, particularly using DBT in BigQuery.
• Experience with Terraform to define and manage cloud infrastructure through code.

Desirables:

• Familiarity with AWS, GCP or other cloud providers.
• Experience with containerisation technologies (Docker, Kubernetes).
• Familiarity with streaming data ingestion technologies (Kafka, Debezium).
• Exposure to feature store concepts and practices (Feast or similar).

Interview process

Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you! Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general you can expect the below, following a chat with one of our Talent Team:

• Stage 1 - 30 mins with one of the team
• Stage 2 - Take-home challenge
• Stage 3 - 60 mins technical interview with two team members
• Stage 4 - 45 min final with two data executives