about the company.
Top 500 global company
about the team.
Global team
responsibilities:
- Define and evolve the reference engineering standards for core data provisioning platforms, ensuring alignment to the bank’s Group Data Strategy and Data Future State Architecture.
- Provide deep technical standards and approach leadership across key platforms:
- Enterprise Data Assets (EDA) and APIs for standardized data provisioning
- Reference Data Services for golden source alignment and hierarchy management
- Big Data and Movement technologies for ingestion, streaming, and transformation at scale
- Decisioning Infrastructure including real-time feature stores and orchestration
- Lead peer reviews and ensure platform consistency, reusability, and compliance with enterprise standards.
- Guide development teams in implementing data pipelines, APIs, metadata-driven controls, and automated testing frameworks.
- Champion DevSecOps, CI/CD, and infrastructure-as-code across engineering teams.
- Drive the control transition to using agentic AI coding assistants.
- Lead the technology Developer Experience, enabling the whole of technology to discover, understand and onboard themselves to our services.
- Drive best practices in observability, performance engineering, cost optimization, and scalability in hybrid cloud environments.
- Mentor senior engineers and influence technical delivery across multiple squads and geographies.
- Collaborate with Architecture, Cybersecurity, CDO, and business product owners to align engineering outcomes to strategic objectives.
- Represent the domain in cross-platform technology councils and architecture forums.
skills and experience required.
- 15+ years of hands-on experience in data platform engineering, architecture, or software development, preferably in a global financial institution or scaled enterprise.
- Expertise in building large-scale data pipelines, APIs, and event-driven systems using modern frameworks and technologies (e.g., Spring, Kafka, Spark, etc).
- Strong architectural understanding of data mesh, data lakehouse, and real-time, customer-facing operational & analytics platforms.
- Experience with cloud platforms (AWS and GCP), containerisation, and IaC tools.
- Deep familiarity with development tooling and automation covering modeling, observability, resilience, and , and governance patterns.
- Strong track record of building reusable platform services that accelerate delivery for data consumers and AI/ML initiatives.
- Experience with enterprise reference data management and integration is highly desirable.
- Excellent communication and influencing skills, with the ability to engage and guide both engineering teams and non-technical stakeholders.
