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

• Define and maintain the end-to-end application and data architecture • Establish standards for system design, integration patterns (APIs, middleware, eventing), and data models • Ensure scalability, performance, and long-term maintainability • Act as the technical lead across all external development partners • Review and approve solution designs, code architecture, and technical approaches • Challenge vendors where needed — no rubber stamping • Ensure delivery aligns with architectural standards and business outcomes • Define and oversee data architecture, governance, and quality standards • Manage integration across systems (ERP, CRM, eCommerce, etc.) • Ensure data is usable, reliable, and decision-ready • Partner with leadership to translate business goals into technical roadmaps and system requirements • Simplify complex technical concepts for non-technical stakeholders • Evaluate and guide decisions on SaaS vs. custom development, build vs. buy vs. integrate • Design integration architecture across ERP, marketing systems, and data platforms • Establish architecture governance processes • Participate in sprint reviews, backlog prioritization, and delivery checkpoints • Ensure proper documentation, testing, and deployment standards