Data Engineer at Wise

✨ The Stack take on this Wise job

• 🏢 Wise has 49 jobs here, 47 with visa sponsorship see all
• 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
• 💡 No salary shown. Ask Wise if it is above the minimum.

Updated 2026-11-05, from our data and official sources.

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About the role

Responsibilities:
- Own and deliver the end-to-end analytics stack for KYC & Onboarding: ingestion, transformation, modelling, dashboards, and alerting.
- Design, build and maintain core datasets (onboarding events, risk scores, alerts, case outcomes) used for detection, monitoring and reporting.
- Define and execute the analytics infrastructure roadmap for Global KYC & Onboarding.
- Implement and evangelise modern tooling and best practices (dbt, Airflow, Snowflake, Python, Looker/Superset) for reliability, testing, versioning and deployment.
- Instrument pipelines with monitoring, error-handling and data-quality checks appropriate for a regulated environment.
- Partner with product, compliance, risk, analytics and ops to translate data into actionable narratives and KPIs (e.g., time-to-onboard, false-positive rate).
- Identify and onboard new data sources, design tagging strategies and support remediation and control workflows.

Requirements:
- Proven experience building analytics pipelines and data platforms in production using dbt, Airflow, Snowflake and Python.
- Strong data modelling skills and experience producing trusted, documented datasets for analytics and BI.
- Experience designing and implementing data-quality, testing and monitoring practices for sensitive/regulatory domains.
- Familiarity with BI tools (Looker, Superset) and delivering dashboards and stakeholder-ready metrics.
- Excellent cross-functional communication to work with compliance, product and operations teams.
- Comfort with SQL and quantitative troubleshooting; experience with risk scoring, fraud or financial crime detection is a strong plus.
- 5+ years in data engineering or related roles, with a track record of shipping reliable analytics that drive business decisions.

Responsibilities

Lead the data engineering pipelines for the KYC and onboarding domain to detect and prevent financial crime. Build and maintain core datasets and analytics infrastructure to provide actionable insights for stakeholders.

Requirements

Experience in building data infrastructure and pipelines using modern tools like dbt, Airflow, and Snowflake. Ability to partner with cross-functional teams to define KPIs and implement data-quality best practices in a regulatory environment.

Key skills

• Data Engineering — in 120 other listings on The Stack
• Data Modelling — in 24 other listings on The Stack
• Python — in 1200 other listings on The Stack
• Snowflake — in 41 other listings on The Stack
• Dbt — in 102 other listings on The Stack
• Airflow — in 95 other listings on The Stack
• Looker — in 44 other listings on The Stack
• Superset — in 18 other listings on The Stack
• Analytics Pipelines
• KPI Design
• Data Quality Monitoring
• Financial Crime Detection

On The Stack (as of 2026-11-05)

• Wise has 48 other listings on The Stack, 46 of them marked as offering visa sponsorship.
• 214 visa-sponsored jobs in the United Kingdom on The Stack, posted in the last 60 days.
• 47 visa-sponsored data engineer jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Wise

Is this Data Engineer role still open?

It was posted on 2026-07-28. Listings more than 30 days old may no longer be active, so check Wise's site before applying.

Does Wise offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Wise's site.

Where is this role located?

The listing gives the location as London, United Kingdom. It is marked on-site.

What skills does this role ask for?

The listing names: Data Engineering, Data Modelling, Python, Snowflake, Dbt, Airflow, Looker, Superset, Analytics Pipelines, KPI Design, Data Quality Monitoring, Financial Crime Detection.