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Senior Software Engineer II - Data Onboarding & Reporting

London, England, United Kingdom

Pay
£111,000–145,000/yearAnnual period assumed — pay source
What do we offer: Starting salary: £111k - £145k + RSUs Wise Benefits
Read the full posting
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
Apply at Wise

What you’ll work on

Full posting

Wise is on a mission to create money without borders—making it instant, transparent, and eventually free.

We are looking for a talented Senior Software Engineer II to join our Data Onboarding Team in London.

  • Design and optimize end-to-end data pipelines on the Trino + Iceberg lakehouse

  • Refine raw event logs into verified subledgers and structured Gold metrics

  • Build streaming pipelines on Kafka to meet data freshness targets

  • Embed automated reconciliation checks such as balance and double-entry validations

  • Turn finance needs into production data schemas with controllers and analysts

From the employer’s posting
Wise is on a mission to create money without borders—making it instant, transparent, and eventually free. To support this rapid global growth, our Finance Squad ensures that our financial processes run in an efficient, scalable, and controlled manner.
We are looking for a talented Senior Software Engineer II to join our Data Onboarding Team in London. In this role, you will be the driving force behind building, scaling, and optimizing the critical data pipelines on top of our Lakehouse (Trino + Iceberg) infrastructure following a strict Medallion Architecture (Bronze, Silver, Gold layers). You will build robust data pipelines that serve as the foundational bedrock for reporting and assurance across the entire Finance squad.
Design, build, and optimize robust end-to-end data pipelines on top of our Trino + Iceberg Lakehouse stack.
progressively refine raw event logs (Bronze) into verified subledgers (Silver) and structured Gold metrics
Work closely with high-throughput messaging queues (Kafka) and real-time computation models to maintain our data freshness SLAs.
Embed rigorous, automated reconciliation checks within the data pipelines (e.g., balance and double-entry validations)
Partner directly with finance controllers, analysts, product and engineering to turn complex corporate finance needs into production-ready data schemas.

Tools in this posting

  • Java
  • Python
  • Scala
  • SQL
  • dbt
  • Delta Lake
  • Iceberg
  • Kafka
  • Snowflake
  • Trino
  • Airflow
Source — Tool mentions in context
- Stream/Message Queues: Solid understanding of event-driven architectures and streaming processing via Apache Kafka. - Strong Programming Foundations: Proficiency in Python, SQL, or an Object-Oriented language (Java/Scala). Nice to Haves
- Advanced Data Modeling: Deep expertise in Medallion Architecture patterns, Star Schemas, and processing semi-structured financial data logs (JSON/Avro). - Robust ETL/ELT Orchestration: Expert proficiency in data transformation engines (such as dbt) and scheduling tools (such as Airflow). - Stream/Message Queues: Solid understanding of event-driven architectures and streaming processing via Apache Kafka.
Must Haves (Hard Skills) - Data Lakehouse Mastery: Proven experience building data infrastructure using Trino / Presto and open table formats like Apache Iceberg or Delta Lake. - Advanced Data Modeling: Deep expertise in Medallion Architecture patterns, Star Schemas, and processing semi-structured financial data logs (JSON/Avro).
Wise is on a mission to create money without borders—making it instant, transparent, and eventually free. To support this rapid global growth, our Finance Squad ensures that our financial processes run in an efficient, scalable, and controlled manner. We are looking for a talented Senior Software Engineer II to join our Data Onboarding Team in London. In this role, you will be the driving force behind building, scaling, and optimizing the critical data pipelines on top of our Lakehouse (Trino + Iceberg) infrastructure following a strict Medallion Architecture (Bronze, Silver, Gold layers). You will build robust data pipelines that serve as the foundational bedrock for reporting and assurance across the entire Finance squad. The Finance Squad is responsible for steward-shipping the financial truth of Wise. The Data Onboarding team operates alongside Core Accounting, Finance Implementation, and Data & Assurance. Our team's mandate is to abstract product complexity and build reusable, domain-agnostic stream-enrichment pipelines. We handle high-throughput financial event data (processing hundreds of millions of events daily) to bridge the gap between immutable product events and audit-ready data structures.
Technical Leadership & Pipeline Engineering - Lakehouse Architecture: Design, build, and optimize robust end-to-end data pipelines on top of our Trino + Iceberg Lakehouse stack. - Medallion Implementation: Own the extraction, cleaning, and modeling layers to progressively refine raw event logs (Bronze) into verified subledgers (Silver) and structured Gold metrics.
- Medallion Implementation: Own the extraction, cleaning, and modeling layers to progressively refine raw event logs (Bronze) into verified subledgers (Silver) and structured Gold metrics. - Stream Processing: Work closely with high-throughput messaging queues (Kafka) and real-time computation models to maintain our data freshness SLAs. - Data Quality & Assurance: Embed rigorous, automated reconciliation checks within the data pipelines (e.g., balance and double-entry validations) to catch data discrepancies before they reach financial reporting layers.
- Robust ETL/ELT Orchestration: Expert proficiency in data transformation engines (such as dbt) and scheduling tools (such as Airflow). - Stream/Message Queues: Solid understanding of event-driven architectures and streaming processing via Apache Kafka. - Strong Programming Foundations: Proficiency in Python, SQL, or an Object-Oriented language (Java/Scala).
- Experience operating data assets within a regulated SOX/PCAOB compliance environment. - Familiarity with cloud data warehouses (e.g., Snowflake) and cloud compute orchestration. - Prior domain exposure to financial flows, double-entry ledger systems, or accounting integrations.

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First seen by us
Oct 28, 2026 · today
Recorded sightings
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Reposted
once since Jul 31, 2026

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Job description

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Job Description

About the Role:

Wise is on a mission to create money without borders—making it instant, transparent, and eventually free. To support this rapid global growth, our Finance Squad ensures that our financial processes run in an efficient, scalable, and controlled manner.

We are looking for a talented Senior Software Engineer II to join our Data Onboarding Team in London. In this role, you will be the driving force behind building, scaling, and optimizing the critical data pipelines on top of our Lakehouse (Trino + Iceberg) infrastructure following a strict Medallion Architecture (Bronze, Silver, Gold layers). You will build robust data pipelines that serve as the foundational bedrock for reporting and assurance across the entire Finance squad.

The Finance Squad is responsible for steward-shipping the financial truth of Wise. The Data Onboarding team operates alongside Core Accounting, Finance Implementation, and Data & Assurance. Our team's mandate is to abstract product complexity and build reusable, domain-agnostic stream-enrichment pipelines. We handle high-throughput financial event data (processing hundreds of millions of events daily) to bridge the gap between immutable product events and audit-ready data structures.

What will you be working on?

Technical Leadership & Pipeline Engineering

  • Lakehouse Architecture: Design, build, and optimize robust end-to-end data pipelines on top of our Trino + Iceberg Lakehouse stack.

  • Medallion Implementation: Own the extraction, cleaning, and modeling layers to progressively refine raw event logs (Bronze) into verified subledgers (Silver) and structured Gold metrics.

  • Stream Processing: Work closely with high-throughput messaging queues (Kafka) and real-time computation models to maintain our data freshness SLAs.

  • Data Quality & Assurance: Embed rigorous, automated reconciliation checks within the data pipelines (e.g., balance and double-entry validations) to catch data discrepancies before they reach financial reporting layers.

Strategic Impact & Collaboration

  • Product & Stakeholder Alignment: Partner directly with finance controllers, analysts, product and engineering to turn complex corporate finance needs into production-ready data schemas.

  • Platform Alignment: Address data scaling constraints, structural technical debt, and compute optimizations to directly align infrastructure costs with Wise's financial targets.

Mentorship & Engineering Excellence

  • Upskilling: Assist and coach mid-level/junior engineers within the team, performing thorough code and technical design reviews.

Must Haves (Hard Skills)

  • Data Lakehouse Mastery: Proven experience building data infrastructure using Trino / Presto and open table formats like Apache Iceberg or Delta Lake.

  • Advanced Data Modeling: Deep expertise in Medallion Architecture patterns, Star Schemas, and processing semi-structured financial data logs (JSON/Avro).

  • Robust ETL/ELT Orchestration: Expert proficiency in data transformation engines (such as dbt) and scheduling tools (such as Airflow).

  • Stream/Message Queues: Solid understanding of event-driven architectures and streaming processing via Apache Kafka.

  • Strong Programming Foundations: Proficiency in Python, SQL, or an Object-Oriented language (Java/Scala).

Nice to Haves

  • Experience operating data assets within a regulated SOX/PCAOB compliance environment.

  • Familiarity with cloud data warehouses (e.g., Snowflake) and cloud compute orchestration.

  • Prior domain exposure to financial flows, double-entry ledger systems, or accounting integrations.

Soft Skills

  • A strong sense of long-term technical ownership.

  • Excellent communication skills with the ability to bridge technical engineering realities with financial analyst requirements.

What We Offer / Selling Points

  • The rare opportunity to help engineer the core "Engine of Trust" for a fast-growing global fintech moving billions in cross-border volume.

  • A chance to work beyond isolated data pipelines, building reusable libraries and system-wide framework layers rather than bespoke, one-off fixes.

  • Direct interaction and collaboration with top-tier product minds, data platform engineers, and global financial stakeholders.
     

Interested? Find out more:


What do we offer: 

Additional Information

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.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

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.
For everyone, everywhere.

More about our mission and what we offer.

Source details

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Pay
What do we offer: Starting salary: £111k - £145k + RSUs Wise Benefits
Location & working pattern

London, England, United Kingdom

- Wise Benefits - #LI-AB3 #LI-Hybrid - Paved Paths: Create structured framework documentation and automated tooling to reduce onboarding friction for new data sources.
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