Senior Data Engineer I - Global KYC & Onboarding

D8 LooP; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Based in London, you will collaborate with product and compliance teams to scale data platforms, improve performance, and provide accurate analytics for onboarding and KYC decisions.

Qualifications

• Airflow pipelines built, scheduled, and monitored in production.
• Domain data modelling for onboarding funnels and verification events.
• Strong Python software engineering skills for data-intensive tasks.
• Advanced SQL and data modelling for large-scale datasets.
• Observability and auditability for regulatory compliance and product health.
• Performance optimization to resolve latency and ensure data consistency.
• ETL/ELT pipelines ingesting high-volume event data.
• Define shared data contracts and automated testing frameworks.

Skills

Airflow

Domain data modelling

Python

SQL

Observability

Performance optimization

Streaming data ingestion

Governance & security

Data engineering standards

Spark

dbt

Tools

Kafka

Snowflake

Presto/Trino

Job description

Wise · api.smartrecruiters.com · checked today

Senior Data Engineer I - Global KYC & Onboarding

Wise is a global technology company, building the best way to move and manage the world’s money. 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.

Skills, with evidence

• Airflow / orchestration Pipeline & Workflow Orchestration: Proven track record building, scheduling, and monitoring production pipelines using modern workflow orchestrators such as Apache Airflow. must have
• 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. must have
• Python Strong Python Fundamentals: Solid software engineering skills in Python with the ability to write clean, maintainable code, navigate complex algorithms, and optimize data-intensive tasks for performance and computational efficiency. must have
• SQL Advanced SQL & Data Modelling: Extensive experience designing normalized and dimensional data models, paired with strong proficiency in crafting, debugging, and tuning complex SQL queries over large-scale datasets. must have
• 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.
• Cost & performance Improve Performance & Scale: Optimise data-intensive workflows and queries, resolving latency bottlenecks and ensuring data consistency across personal, business, and platform onboarding spaces.
• Streaming 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.
• Governance & security 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. not practised here
• 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.
• 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.
• Spark Familiarity with distributed compute and storage platforms (e.g., Kafka, Spark, Snowflake, or Presto/Trino).
• dbt Hands-on experience with modern data transformation tools like dbt.
• Machine learning 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. not practised here