Location
London

Hours
Full-time - 35 hours per week
Hybrid working style, requiring at least two days per week (40% of time) at the London office.

Salary
£104,686 - £123,160

About the Role
Join Lloyds Banking Group, an innovative and fast-changing organisation shaping finance as a force for good. Serving over 26 million customers through brands like Lloyds Bank, Halifax, Bank of Scotland, and Scottish Widows, we are committed to helping Britain prosper and becoming the best bank for customers.

As part of our transformation, we are modernising our data estate by migrating from on-premise platforms to the cloud and enabling Generative AI and Agentic AI through trusted, well-modelled data.

You will join the Data Transformation & Modelling Lab, a key part of our Data & Machine Learning Platform, which shapes the bank's future data landscape by designing and maintaining the Group Data Model, building high-quality reusable data products, developing toolkits for rapid and secure data migration to Google Cloud Platform (BigQuery), and underpinning our AI strategy.

Working in agile, cross-functional teams following Scrum principles, you will help define technical direction, mentor engineers, and deliver foundational data capabilities that power the bank's digital and AI-driven future.

Key responsibilities include designing and evolving data models aligned to business domains and data mesh principles, collaborating with domain teams to deliver reusable data products on BigQuery, leading the transformation of legacy data models to cloud-native equivalents, optimising data models for performance and cost-efficiency, supporting modelling standards and best practices, partnering with architects and product owners, and mentoring junior engineers.

Experience
- Expertise in conceptual, logical, and physical data modelling, including dimensional modelling and metadata management, with experience using tools such as ER Studio.
- Experience designing models for analytical and operational use cases in regulated environments.
- Strong understanding of canonical modelling, schema evolution, and data contract design.
- Hands-on experience with Google BigQuery, including query optimisation, partitioning, clustering, and federated queries.
- Familiarity with GCP services such as Cloud Storage, Dataflow, Pub/Sub, and Looker.
- Experience building scalable data pipelines and ELT workflows in cloud-native environments.
- Knowledge of data mesh principles, decentralized governance, and user-friendly infrastructure.
- Experience collaborating with domain teams to define and publish data products.
- Proficiency in SQL and Python; experience with dbt, Dataform, or similar ELT tools.
- Familiarity with orchestration tools like Airflow or Cloud Composer.
- Exposure to CI/CD, Git-based workflows, and infrastructure-as-code (e.g., Terraform).
- Experience with data cataloging, lineage, and quality tools (e.g., Collibra, Alation, GCP Data Catalog).
- Strong understanding of data security, access control, and compliance frameworks (e.g., GDPR, BCBS 239).
- Proven ability to lead technical design discussions and influence architectural decisions.
- Strong stakeholder engagement skills and ability to translate business needs into scalable data solutions.
- Experience mentoring and coaching engineers and modellers.
- Ability to lead multi-disciplinary, geographically distributed teams across project lifecycles.

About you
You are a technically skilled and collaborative leader passionate about data modelling and cloud engineering. You thrive in agile environments, enjoy mentoring others, and are motivated by shaping innovative data solutions that support AI and digital transformation. You value inclusion and diversity and are committed to fostering a culture of continuous learning and technical excellence.

Qualifications
Relevant experience and expertise in data modelling, cloud engineering, and leadership are essential. Formal qualifications are not explicitly stated but a strong professional background in related technologies and methodologies is expected.