Senior ML Data Engineer — Real-Time Feature Pipelines

Hub; København; Hybrid; DKK 900,000 - 1,100,000; Full time

Benefits offered by this job

Birthday leave

30 days vacation

Pension and insurance

Weekly lunch scheme

Snacks and beverages

Job summary

GoWish is seeking a Senior ML Data Engineer to own the offline-to-online feature infrastructure powering our real-time recommendation and search engines. You will build low-latency data syncs, feature-serving layers, and bridge analytical warehouses with operational stores like AlloyDB and Redis.

You will work on pipeline orchestration (Airflow, dbt), Infrastructure as Code (Terraform, Docker), and database tuning to ensure sub-second queries for millions of users.

Qualifications

• 4+ years of experience in ML data engineering or a closely related field.
• Experience building offline-to-online feature pipelines for real-time ML systems.
• Proficiency in Airflow, dbt, and Terraform for data infrastructure.
• Strong SQL skills and database design for OLAP/OLTP workloads.

Responsibilities

• Build and operate offline-to-online feature pipelines syncing data from analytical warehouses to online stores to support real-time ML models.
• Design and maintain robust Airflow DAGs and modular dbt transformations to meet low-latency SLAs.
• Provision and automate cloud data infrastructure using Terraform and Docker in CI/CD pipelines.
• Collaborate with Data Scientists and Backend Engineers to define online/offline feature contracts and optimize database schemas for sub-second queries.

Skills

Airflow

dbt

Terraform

Docker

SQL

Feature Stores

BigQuery

Redis

Low-latency systems

Education

Bachelor's degree in a relevant field

Tools

Kubernetes

Kafka

ClickHouse

Job description

GoWish is seeking a Senior ML Data Engineer to own the offline-to-online feature infrastructure powering our real-time recommendation and search engines. You will build low-latency data syncs, feature-serving layers, and bridge analytical warehouses with operational stores like AlloyDB and Redis.

You will work on pipeline orchestration (Airflow, dbt), Infrastructure as Code (Terraform, Docker), and database tuning to ensure sub-second queries for millions of users.