Senior ML Data Engineer – Real-Time Features & Low-Latency

Ønskeskyen; København; Hybride; DKK 900 000 - 1 500 000; Plein temps

Avantages offerts par ce poste

Birthday leave

30 days vacation

Pension and insurance

Weekly lunch scheme

Snacks and beverages

Résumé du poste

GoWish in Østerbro, Copenhagen, is seeking a Senior ML Data Engineer to own the offline-to-online feature infrastructure powering real-time recommendations and search at scale.

You will build the low-latency data syncs and feature-serving layers bridging analytical warehouses with operational stores, using Airflow, dbt, Terraform, and Docker, while collaborating with Data Scientists and Backend Engineers to ensure sub-second queries and reliable data delivery.

Qualifications

• 4+ years of experience
• Operational data pipelines from warehouses to online stores
• Infrastructure as code with Terraform and Docker
• Pipeline orchestration with Airflow or Dagster
• Advanced SQL for OLAP/OLTP and performance tuning

Responsabilités

• Build and operate resilient feature pipelines syncing data from analytical warehouses to online stores for real-time ML models
• Design and maintain Airflow DAGs and modular dbt transformations for low-latency feature generation
• Provision, scale, and automate cloud data infrastructure using Terraform, Docker, and CI/CD
• Collaborate with Data Scientists and Backend Engineers to define online/offline feature contracts and optimize schemas

Connaissances

Feature pipelines

Airflow

dbt

Terraform

Docker

SQL tuning

BigQuery

Redis

Feature Stores

Performance tuning

Outils

Airflow

dbt

Terraform

Docker

BigQuery

AlloyDB

Redis

Kafka

Flink

Dagster

Description du poste

GoWish in Østerbro, Copenhagen, is seeking a Senior ML Data Engineer to own the offline-to-online feature infrastructure powering real-time recommendations and search at scale.

You will build the low-latency data syncs and feature-serving layers bridging analytical warehouses with operational stores, using Airflow, dbt, Terraform, and Docker, while collaborating with Data Scientists and Backend Engineers to ensure sub-second queries and reliable data delivery.