Drive Data Architecture & Resilience: audit and enhance data pipelines and streaming architectures; establish monitoring, reliability, and scale using Python, dbt, Airflow, Kafka, and Trino/Iceberg.
Build the Unified Customer View: productionize PoCs into scalable Airflow/dbt workflows to support CDP datasets.
Bridge Engineering & Analytics: set data ingestion, cleansing, and transformation standards, applying an analytics engineering mindset (dbt) to raw data.
Own MarTech Infrastructure: collaborate to bring MarTech ownership (e.g., Braze, HighTouch reverse ETL) in-house, delivering end-to-end data solutions.
Technical Scoping & Discovery: define broad problem spaces, propose robust architectures, and execute roadmaps.

Key requirements

Drive Data Architecture & Resilience: audit and enhance data pipelines and streaming architectures; establish monitoring, reliability, and scale using Python, dbt, Airflow, Kafka, and Trino/Iceberg.
Build the Unified Customer View: productionize PoCs into scalable Airflow/dbt workflows to support CDP datasets.
Bridge Engineering & Analytics: set data ingestion, cleansing, and transformation standards, applying an analytics engineering mindset (dbt) to raw data.
Own MarTech Infrastructure: collaborate to bring MarTech ownership (e.g., Braze, HighTouch reverse ETL) in-house, delivering end-to-end data solutions.
Technical Scoping & Discovery: define broad problem spaces, propose robust architectures, and execute roadmaps.

Jobs at Wise

4 active roles from this employer in the JobMatcher catalog.

All company jobs

Jobs at Wise

4 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Wise is seeking a Senior Data Engineer II to join the Scalable Growth team within the Global Product Tribe. Based in London, United Kingdom and on-site, you will serve as the technical authority on data architecture, bridging platform engineering and analytics engineering. You will build and scale data infrastructure to support high-volume streaming and batch data for Wise's global marketing engine and help enable a unified customer view. You will mentor engineers and influence how Wise attracts millions of customers worldwide. This role requires collaboration with the Marketing Platform team, cross-functional partners, and external vendors to transform marketing data into a scalable platform product.

Responsibilities

Qualifications:

Drive Data Architecture & Resilience: audit and enhance data pipelines and streaming architectures; establish monitoring, reliability, and scale using Python, dbt, Airflow, Kafka, and Trino/Iceberg.
Build the Unified Customer View: productionize PoCs into scalable Airflow/dbt workflows to support CDP datasets.
Bridge Engineering & Analytics: set data ingestion, cleansing, and transformation standards, applying an analytics engineering mindset (dbt) to raw data.
Own MarTech Infrastructure: collaborate to bring MarTech ownership (e.g., Braze, HighTouch reverse ETL) in-house, delivering end-to-end data solutions.
Technical Scoping & Discovery: define broad problem spaces, propose robust architectures, and execute roadmaps.
Coach & Mentor: uplift the data capabilities of software engineers through code reviews and guidance.
Python

About the role

Wise is seeking a Senior Data Engineer II to join the Scalable Growth team within the Global Product Tribe. Based in London, United Kingdom and on-site, you will serve as the technical authority on data architecture, bridging platform engineering and analytics engineering. You will build and scale data infrastructure to support high-volume streaming and batch data for Wise's global marketing engine and help enable a unified customer view. You will mentor engineers and influence how Wise attracts millions of customers worldwide. This role requires collaboration with the Marketing Platform team, cross-functional partners, and external vendors to transform marketing data into a scalable platform product.

Responsibilities

Qualifications:

Drive Data Architecture & Resilience: audit and enhance data pipelines and streaming architectures; establish monitoring, reliability, and scale using Python, dbt, Airflow, Kafka, and Trino/Iceberg.
Build the Unified Customer View: productionize PoCs into scalable Airflow/dbt workflows to support CDP datasets.
Bridge Engineering & Analytics: set data ingestion, cleansing, and transformation standards, applying an analytics engineering mindset (dbt) to raw data.
Own MarTech Infrastructure: collaborate to bring MarTech ownership (e.g., Braze, HighTouch reverse ETL) in-house, delivering end-to-end data solutions.
Technical Scoping & Discovery: define broad problem spaces, propose robust architectures, and execute roadmaps.
Coach & Mentor: uplift the data capabilities of software engineers through code reviews and guidance.
Python

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