Senior Data Engineer

GoWish is reinventing gifting for millions with bold ideas, playful collaboration, and products that make a real impact - join the journey and help shape the future of social shopping.

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About GoWish, Ønskeskyen ☁️

At GoWish, we’re building the world’s leading wish-list and social shopping platform - a place where you can make a real impact and have fun doing it.

We’re on a mission to reinvent gifting for millions. We win together, driven by curiosity, ownership, and bold ideas that turn into real products. We experiment cross-functionally, push for excellence, and keep things playful as we grow - together.

Team & role overview

We are looking for a systems-minded Senior Machine Learning Data Engineer to our AI team to own the offline-to-online feature infrastructure that powers our real-time recommendation and search engines at scale. You will be building the low-latency data syncs and feature-serving layers that bridge our analytical warehouses with operational databases, caches, and Feature Stores.

This is a role for a performance-driven engineer who excels at pipeline orchestration (Airflow, dbt), Infrastructure as Code (Terraform, Docker), and database tuning, ensuring our recommendation systems receive reliable data under strict low-latency SLAs.

If you thrive on solving complex data synchronization challenges, scaling operational storage for millions of users, and acting as the operational bridge between Data Science and Backend Engineering - you will play a critical role in powering our product's core ML capabilities.

What you'll do

• Offline-to-Online Feature Pipelines: Build and operate resilient feature pipelines syncing transformed data from analytical warehouses (e.g., BigQuery) into operational storage layers (e.g., AlloyDB, Redis, Feature Stores) to serve real-time ML recommendation models at scale.
• Pipeline Orchestration and Transformations: Design, test, and maintain robust Apache Airflow DAGs and modular dbt transformations to automate end-to-end data ingestion and feature generation under strict low-latency SLAs.
• Infrastructure as Code and Reliability: Provision, scale, and automate cloud data infrastructure using Terraform, Docker, and CI/CD pipelines, implementing automated data quality testing and monitoring across all feature syncs.
• Cross-Functional System Design: Partner directly with Data Scientists and Backend Engineers to define online/offline feature contracts, optimize database schemas for sub-second queries, and eliminate training-serving skew for millions of active users.

What we’re looking for 🌟

We imagine that you have

• 4+ years of experience
• Operational Data Pipelines: Proven experience moving batch features from analytical warehouses (BigQuery, Snowflake) into low-latency online stores (AlloyDB, Redis, Feature Stores) to serve real-time ML recommendation models.
• Infrastructure as Code (IaC): Deep hands-on experience provisioning, scaling, and managing cloud data infrastructure using Terraform, Docker, and modern CI/CD automation pipelines.
• Pipeline Orchestration & Transformations: Strong track record building, scheduling, and maintaining workflow orchestrations (e.g., Airflow, Dagster) and modular data transformation frameworks (e.g., dbt).
• Database Performance Tuning: Advanced SQL skills across both OLAP and OLTP systems. Ability to optimize schemas, indexing, partitioning, and queries.

It is further seen as an advantage to have experience with

• Production Feature Management: Experience managing Feature Stores (e.g., Feast, Tecton) or low-latency key-value stores (e.g., Redis) to serve online ML features with zero training-serving skew.
• Real-Time OLA): Experience with ClickHouse, Pinot, Druid or others for ultra-fast aggregations over live event streams.
• Event Streaming: Hands-on experience with streaming tools like GCP Pub/Sub, Kafka, or Flink for real-time feature updates.

Why GoWish should sit at top of your career wish list 💙

At GoWish, we believe great work comes from people who feel energized, supported, and excited to show up every day. That’s why we’ve built a workplace where celebration, growth, and genuine enjoyment are part of the journey - not perks on the side.

Take your birthday off with Happy Birthday Leave , enjoy 30 days of vacation to fully recharge, and join a team that fills the year with events and playful happenings. We invest in your future with pension and insurance , fuel your days with a weekly lunch scheme , and keep you going with unlimited snacks and sodas - because work should feel good.

At GoWish, you don’t just build great products. You build a great work life.

Practical information

We collaborate in a flexible hybrid setup, with most of our magic happening at our bright, modern office in Østerbro - perfectly placed between Nordhavn and Østerport Station. We’re ready for you to join us as soon as you are.

👉 Ready to help shape a platform that already empowers 18+ million users (and counting)? Join us at GoWish - and let’s build the future of data-driven innovation together.

Don’t wait to apply - we review applications continuously and may close the position once the right candidate is found.

Department

Technology

Location

Copenhagen

About GoWish, Ønskeskyen

GoWish is a global wishlist and social shopping platform connecting millions of people worldwide through the things they truly wish for.

Founded in 2015 as Ønskeskyen, GoWish is today trusted across generations worldwide to make wishing, gifting, and product discovery easier and more enjoyable. From creating and sharing wishlists to finding inspiration and discovering new products, GoWish brings people together around what matters to them.

With best-in-class wishlist features and inspiring content, GoWish removes the friction from gifting and shopping. No more endless scrolling, double gifting, or unnecessary returns.

Here, inspiration isn’t random – it’s smart, it’s social. And it’s made for you. By blending technology and community, GoWish turns every discovery into something personal, and every gift into something special.

Founded in 2020

Co-workers 120