The purpose of this role is to design, build, and maintain scalable data pipelines and infrastructure that enable the efficient processing and analysis of large, complex data sets. This role is designed for impact, and we believe our best work happens when we connect. While we operate a flexible model, we expect you to spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops. • Develop and maintain automated data processing pipelines using Google Cloud: - Design, build, and maintain data pipelines to support data ingestion, ETL, and storage - Build and maintain automated data pipelines to monitor data quality and troubleshoot issues • Implement and maintain databases and data storage solutions: - Stay up-to-date with emerging trends and technologies in big data and data engineering - Ensure data quality, accuracy, and completeness • Implement and enforce data governance policies and procedures to ensure data quality and accuracy: - Collaborate with data scientists and analysts to design and optimise data models for analytical and reporting purposes - Develop and maintain data models to support analytics and reporting - Monitor and maintain data infrastructure to ensure availability and performance

Role Description As a Data Engineer, you will play a key role in creating, migrating, and maintaining regulatory reporting systems on Google Cloud Platform (GCP). - The Regulatory Data team is responsible for building and maintaining near real-time and batch reporting systems to fulfill regulatory requirements. - The team utilizes complex business logic to transform internal data models into those required by industry regulators and third parties. - You will support the migration from on-premise SQL Server systems to GCP as the team creates new GCP reporting systems. - Develop new cloud-native solutions and explore AI-driven development and automation to improve efficiency, consistency, and reliability across the team's processes. Qualifications - Experience with Google BigQuery. - SQL development experience, with the ability to write complex queries, stored procedures, and performance-tune data-intensive workloads. - Familiarity with GCP services such as Cloud Storage, Cloud Functions, Pub/Sub, or Cloud Composer. - Experience with data pipeline development (ETL/ELT) and orchestration tooling. - Exposure to Infrastructure as Code such as Terraform, and CI/CD pipelines such as Git, GitLab. - Methodical, with high attention to detail and the ability to break down complex requirements into simple solutions. - Interest in AI-assisted development tooling, such as Claude Code. - Ability to work to deadlines in a fast-paced, reactive environment. - SQL Server experience to support legacy systems during migration. Requirements - Developing and maintaining regulatory data submission systems, both existing on-premise and new cloud-based solutions in GCP/BigQuery. - Building and maintaining automated data pipelines for ingestion, transformation, and loading of regulatory data. - Supporting the migration of legacy SQL Server solutions to BigQuery, working closely with seniors and technical leads on architecture and data modelling. - Implementing data validation, monitoring, and alerting to ensure accuracy, consistency, and reliability of regulatory submissions. - Leveraging AI tooling, such as Claude Code, and contributing to the team's automation strategies to reduce manual effort and improve quality. - Participating in code reviews and adhering to departmental standards and the development process. - Conducting QA to ensure solutions are accurate, efficient, and performant. - Creating and maintaining relevant documentation. - Collaborating with other team members to deliver high-quality solutions within required timeframes. Benefits - Creating an environment where everyone feels welcome, respected, and valued. - Opportunities for growth and development, regardless of background. - Commitment to continuous improvement and striving to be better.

• Build data ingestion pipelines to extract and transform enterprise data. • Implement data cleansing and normalization routines. • Write and maintain ETL jobs using Spark/PySpark on cloud infrastructure. • Implement data validation and quality checks at each pipeline stage. • Build automated data export jobs for model training datasets. • Support feature extraction from enterprise schemas. • Monitor pipeline health, troubleshoot failures, and optimize performance. • Document data lineage, schemas, and transformation logic.

• Diseñar, desarrollar, implementar y ajustar sistemas distribuidos a gran escala y canalizaciones que procesan grandes volúmenes de datos • Centrándose en la escalabilidad, la baja latencia y la tolerancia a fallos en cada sistema construido