Senior Data Engineer I - Global KYC & Onboarding Wise London, GB Engineering 1d ago check more →

Neobankbeat; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Job summary

Wise in London is seeking a Senior Data Engineer for the Global KYC & Onboarding squad to design and build data pipelines, storage infrastructure, and analytical models that power onboarding and FinCrime controls. You will connect core engineering with product analytics and transform high-volume events into reliable datasets.

You will modernise the data layer, champion robust ETL/ELT practices, and ensure observability and auditability across verification flows.

Qualifications

• Bridge Engineering & Analytics: Translate raw system inputs into clean, aggregated data models.
• Modernise Verification Data Architecture: Build and standardise production pipelines in Python and Airflow.
• Improve Performance & Scale: Optimise data-intensive workflows and queries to ensure data consistency across onboarding spaces.

Responsibilities

• Bridge Engineering & Analytics: Translate raw system inputs into clean, aggregated data models that empower Product Analysts and Data Scientists to measure KPIs, conversion funnels, and control pass rates.
• Modernise Verification Data Architecture: Build and standardise production pipelines in Python and Airflow, replacing ad-hoc queries and fragmented scripts with robust, automated data infrastructure.
• Improve Performance & Scale: Optimise data-intensive workflows and queries, resolving latency bottlenecks and ensuring data consistency across personal, business, and platform onboarding spaces.

Tools

Python

Airflow

Job description

engineering · Wise# Senior Data Engineer I - Global KYC & OnboardingLondon, GBEngineeringHybridposted 1d agoWe're looking for an experienced Senior Data Engineer to join our Global KYC & Onboarding squad within the FinCrime organisation. You will be instrumental in designing and building the data pipelines, storage infrastructure, and analytical data models that power customer onboarding and financial crime controls across the globe. This role sits directly between core engineering microservices and product analytics. While customer-facing systems fire dozens of raw operational events per verification flow, our analytics and machine learning teams require structured, clean, and reliable metrics to steer the business. You will own the data journey between these two worlds, transforming high-volume operational events into observable, audit-ready analytical datasets.Operating across our squad's three core pillars—Business Onboarding Verification, Personal Onboarding Verification, and Wise Platform Verification (our B2B Wise-as-a-Service solution)—you will be the driving force behind modernising our verification data ecosystem, focusing on:* Data Ingestion & Pipelines: Designing and maintaining robust ETL/ELT pipelines that ingest high-volume, event-driven data from verification services and transform it into reliable analytical stores.* Domain Data Modelling: Mapping complex transactional events into coherent, business-accurate data models that define how customer onboarding funnels, verification triggers, and conversion metrics are tracked.* Platform Modernisation: Collaborating with software engineering to replace 8-year-old legacy verification infrastructure with modern, scalable data flows built for true \"config-change\" flexibility.* Observability & Auditability: Ensuring that every onboarding flow and FinCrime control is measurable, auditable, and transparent out-of-the-box for regulatory compliance and product health monitoring.What will you be working on? You will join the squad at an exciting inflection point. We were the first team at Wise to establish dedicated data engineering, and you’ll join our team of data engineers to scale and professionalise this foundational capability across the entire verification domain. The core challenge for the team—and your immediate focus—is modernising our data layer while the squad delivers the new Onboarding Platform. We don't hide away for two years to build a finished product; we ship iteratively, learn, and replace legacy components in place. You will ensure that as new platform capabilities go live, our data models, pipelines, and reporting layers scale seamlessly alongside them.### Your immediate impact will be to* Bridge Engineering & Analytics: Translate raw system inputs into clean, aggregated data models that empower Product Analysts and Data Scientists to measure KPIs, conversion funnels, and control pass rates without having to untangle backend service logs.* Modernise Verification Data Architecture: Build and standardise production pipelines in Python and Airflow, replacing ad-hoc queries and fragmented scripts with robust, automated data infrastructure.* Improve Performance & Scale: Optimise data-intensive workflows and queries, resolving latency bottlenecks and ensuring data consistency across personal, business, and platform onboarding spaces.* Set Data Engineering Standards: Partner with engineering, analytics, and compliance to define shared data contracts, pipeline monitoring best practices, and automated testing frameworks across the squad.