Lead Engineer, Data Platform

The Data Platform Engineering team builds the platform that Tide's data producers and consumers rely on: backend engineers, data engineers, data scientists and, increasingly, AI agents. We own how data is produced, moved, stored, modelled, governed and served across the UK, EU and India, on GCP with BigQuery at the core. As Lead Engineer, Data Platform, you will be a hands-on individual contributor who leads platform initiatives within a clear technical direction set by the Data Infrastructure team. You will own small-medium platform initiatives and quarterly goals, taking them from design through to shipping and running them in production, and you will work closely with the Data Infrastructure Engineering Manager and the wider platform team.

Responsibilities

Design and deliver self-service paths so engineers can onboard new sources, datasets and pipelines without a ticket queue, with access, masking, quality and cost controls defined in infrastructure as code and CI/CD; Improve ingestion and transformation capabilities so data is fresher and more reliable, handling ordering, duplicates, late data, deletes, replay and backfill, and migrate pipelines without breaking downstream tables; Apply governance in what you build: residency, PII masking, least-privilege and just-in-time access, retention and erasure, and lineage; Set SLOs, freshness and quality checks, and alerting that tells on-call what matters; Find the main cost drivers in data platforms and pipelines, and add guardrails and attribution that teams can act on; Break work into phases that ship, with clear milestones, a migration path and a way to measure adoption; Use AI-assisted development to build and run the platform faster, and help prepare governed, well-described data that agents can use safely; Treat internal engineers as customers: write clear documentation and golden paths, and act on their feedback; Review designs and code for other engineers, and raise the bar for production-grade data and API work

Requirements

Independently designed, shipped and operated a meaningful platform or data infrastructure initiative used by internal engineering teams; Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill); Strong production engineering in Python and SQL, including building and operating APIs, pipelines or shared libraries; Practical infrastructure as code (Terraform or equivalent) and CI/CD for data and infrastructure, including IAM across environments; Working knowledge of data governance in practice: PII masking, access control, residency, retention and lineage; Observability for data pipelines: freshness and quality checks, SLOs, alert thresholds and incident response; The ability to justify technology choices against requirements, with an order-of-magnitude view of cost and capacity; Hands-on use of AI-assisted development with sound engineering judgement; Clear written and verbal communication, and the ability to explain trade-offs to engineers and non-engineers across teams and time zones

Nice to have

Experience with global, decentralised teams; Experience in financial services or other regulated environments; Experience enabling AI tooling for engineering teams (highly preferred)

Benefits

Competitive salary; Generous annual leave on top of bank holidays; Paid maternity, paternity, and adoption leave; Extended unpaid and paid sabbatical options after milestone years; Private family health insurance with additional OPD coverage and top-up options; Comprehensive accidental and life insurance protection; Access to therapy sessions, courses, meditations, and workshops; Paid volunteering and development days annually; Annual learning and development budget; Work from abroad for up to 90 days annually; Contribution towards home office setup; Laptop ownership after replacement; Office snacks, coffee, tea, and lunch (location dependent)