Data Engineer

In this role, you’ll report directly to the Scalable Growth Engineering Lead while embedding day-to-day with the Marketing Platform team. You will serve as the technical authority on data architecture, bridging the gap between platform engineering and analytics engineering. You’ll bring maturity, resilience, and scalability to our data infrastructure-building and shaping systems that handle high-volume streaming and batch data to fuel our global marketing engine.

If you thrive in an environment where you can scope complex problem spaces, mentor engineers in data best practices, and directly impact how Wise attracts millions of customers globally, this is the role for you.

How we work

Scalable Growth is dedicated to building enabling technology that helps Wise acquire customers at the lowest possible cost.

Within this space, the Marketing Platform team owns the data pipelines, internal tools, and integrations with third-party vendors (e.g., Meta, Google) that power our acquisition channels. As our dedicated data lead, you’ll work cross-functionally with engineers, product managers, digital analytics leads, and marketing stakeholders (Paid, Organic, CRM) to transform our marketing data infrastructure into a true platform product.

What will you be working on?

• Drive Data Architecture & Resilience: Audit, map out, and elevate our current data pipelines and streaming architectures. Establish best practices for monitoring, reliability, and scale across our data ecosystem (using Python, dbt, Airflow, Kafka, and Trino/Iceberg).
• Build the Unified Customer View: Productionize PoCs into scalable Airflow/dbt data workflows to lay the groundwork for our Customer Data Platform (CDP) datasets.
• Bridge Engineering & Analytics: Partner with analytics leads to define clear boundaries and standards for data ingestion, cleansing, and transformation-bringing a strong analytics engineering mindset (specifically via dbt) to raw data landing.
• Own MarTech Infrastructure: Work alongside Engineering Leads to bring technology ownership of MarTech systems (e.g., Braze, HighTouch reverse ETL) in-house, building end-to-end solutions rather than isolated data plumbing.
• Technical Scoping & Discovery: Take ownership of broad, ambiguous problem spaces. Uncover hidden challenges, propose robust architectural designs, and execute your own roadmap.
• Coach & Mentor: Elevate the data capabilities of software engineers in the squad through code reviews, architectural guidance, and hands-on mentoring.

We are fully aware that it is uncommon for a candidate to have all skills required, and we fully support everyone in learning new skills with us. So if you have some of those listed below and are eager to learn more, we do want to hear from you!

• Python & Big Data Expertise: Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines (e.g., Kafka Streams, Event Streaming, Trino, Iceberg).
• dbt & Airflow Proficiency: Hands-on experience using dbt for scalable data transformations and Airflow for workflow orchestration.
• Data Architecture Mastery: A strong background in designing scalable, fault-tolerant data architectures, implementing data quality frameworks, and establishing production best practices.
• Product & Discovery Mindset: Ability to take a vague problem statement, independently uncover the requirements, scope project milestones, and drive solutions end-to-end.
• Cross-functional Stakeholder Management: Excellent communication skills with the ability to bridge tech, marketing, and analytics, turning marketing needs into crisp engineering roadmaps.

Nice to Have:

• Experience with MarTech tooling or reverse ETL setups (e.g., HighTouch, Braze, vendor API integrations).
• Familiarity with modern AI/ML data integration practices.
• Background in fintech or fast-scaling tech platform environments.