Data Engineer – Global KYC

Compensation: GBP 105,000 - GBP 130,000 - yearly Company Description Wise is a global technology company, building the best way to move and manage the world's money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. As a Data Engineer in our KYC & Onboarding area, you will own and build the data infrastructure, analytics pipelines and modelling frameworks that detect, prevent and monitor financial crime through customer onboarding. You'll partner closely with product, compliance, analytics, and operations teams to drive data-led insights and proactive controls that enable safe growth. Key Responsibilities Lead data engineering pipeline related to the onboarding / KYC / FinCrime domain; establish best practices around data modelling, testing, monitoring, and deployment. Define and own the analytics infrastructure roadmap for the Global KYC & Onboarding squad, from data source ingestion through to analytics delivery and dashboard creation. Build and maintain core datasets focused on KYC onboarding events, customer risk scores, alert triggers, and case outcomes. Evangelise and lead adoption of modern tooling (e.g., dbt, Airflow, Snowflake, Python, Looker/Superset) to improve reliability, speed, and trust in analytics. Drive implementation of best practices in data-pipeline instrumentation, monitoring, error-handling, and data-quality in a high-stakes regulatory environment. Together with the Analytics and Product team, translate complex data into clear, actionable narratives for key stakeholders – enabling informed decision-making around customer acceptance or decline, risk tiering, and remediation prioritization. Partner with cross-functional teams (compliance, risk, product, operations) to identify new data sources, define tagging strategies, design KPIs (e.g., average time to onboard, false-positive rate), and deliver measurable business impact. Qualifications Required Qualifications & Skills Advanced proficiency in SQL and Python (able to build production-grade analytics pipelines and models). Proven experience in building robust data pipelines and effective data models in a high-volume environment. Hands-on experience with an orchestration tool (e.g., Airflow) or equivalent workflow scheduling. Strong project leadership and stakeholder-management skills – comfortable shaping strategy and delivering on roadmap, with rigorous communication. Experience with BI tools (e.g., Looker, Superset, Tableau) and working closely with analysts and engineers to deliver trusted dashboards. Empathy for stakeholders (compliance, ops, product) and a proactive mindset in identifying and solving their problems in a regulated environment. Nice-to-Have Experience with modern analytics engineering stack: e.g., dbt, Snowflake/BigQuery/Databricks, event-streaming data (Kafka/Kinesis). Previous experience in a fintech, neobank, or payments company with international operations and regulatory scope. Demonstrable expertise in FinCrime / KYC / Onboarding or Risk & Compliance analytics (for example: building models/metrics around onboarding conversion, false‑positive reduction, watch‑list matching, alert triage). Exposure to machine learning or statistical modelling applied to onboarding risk scoring or fraud/AML detection. What We Offer For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive. We're proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers. If you want to find out more about what it's like to work at Wise visit Wise.Jobs. #J-18808-Ljbffr