Data & ML Platform Lead

Overview

In this role you will lead the team that builds and operates the core data and ML infrastructure that powers Curve’s business as it scales with Lloyds Banking Group. You will own platforms for streaming and batch data ingestion, reverse-ETL, real-time decisioning, and ML capabilities used across the organisation. It’s a hands-on leadership position, guiding architecture, reliability, and cloud infrastructure at bank-grade standards. You’ll work with cross-functional teams to drive scalable, observable platforms that enable data‑driven decisions and AI capabilities. This is a high-impact opportunity to shape financial technology infrastructure at scale.

Pay / Benefits

• generous pension contribution up to 15%
• annual bonus award
• share schemes
• discounted shopping
• 30 days’ holiday + bank holidays
• wellbeing initiatives and parental leave

Responsibilities

• Lead and develop a multidisciplinary platform engineering team
• Own design and reliability of Kafka-based decisioning services, data pipelines, orchestration, feature stores and cloud infra
• Ensure platform operates with bank-grade reliability, auditability, and scale
• Support integration of Curve’s platform into Lloyds Banking Group systems
• Hands-on involvement in complex architectural challenges
• Oversee production-grade streaming and batch data ingestion, reverse-ETL, and real-time decisioning

Key requirements

• Significant experience in data/platform engineering with technical leadership
• Production experience with streaming technologies (ideally Kafka) and event-driven architectures at scale
• Experience designing and operating production systems on GCP (BigQuery, Managed Airflow, Cloud Run, Dataflow) or equivalent
• Strong Python programming and at least one systems language (Go preferred)
• Experience building real-time services and batch/streaming data pipelines
• Terraform, Kubernetes and infrastructure as code
• Airflow in production and orchestration platforms
• Experience deploying ML systems in production (feature stores, model serving, ML-driven decisioning)
• Designing observable platforms with SLAs, monitoring, alerting, load testing and capacity planning
• Operating in regulated environments with audit trails and change governance
• Safe deployment approaches (feature flags, shadow modes, staged rollouts, reversible releases)
• Track record of automating operational processes for scalable, reliable systems
• Strong leadership with ability to set direction and develop engineers

• leadership
• stakeholder management
• collaboration
• Kafka
• streaming and event-driven architectures
• GCP technologies: BigQuery, Managed Airflow, Cloud Run, Dataflow

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