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Data Engineer (AI)

Sophia Ferreira

Full-time · Job · Berlin

About the role

We serve over 200 enterprise clients in the retail sector, and every recommendation engine, demand forecast, and pricing model depends on the data infrastructure you'll help build and maintain. Our data team is 5 engineers supporting 3 ML squads — there's no shortage of challenging work. Your primary focus will be building and optimizing Spark-based ETL pipelines that move terabytes of transactional data from source systems into Snowflake, where it's consumed by ML training jobs and analytics dashboards. You'll also own the Airflow DAGs that orchestrate everything. We run a clean, well-documented stack. Code reviews are thorough, tests are expected, and data quality checks are built into every pipeline. If you care about doing data engineering the right way, you'll appreciate how we work.

Responsibilities

  • Build and maintain Spark ETL pipelines processing terabytes daily
  • Design and optimize Snowflake schemas for ML feature stores
  • Own Airflow DAG orchestration and monitoring
  • Collaborate with ML engineers on feature engineering requirements
  • Implement data quality checks and alerting

Requirements

  • 3+ years in data engineering with Python and SQL
  • Production experience with Apache Spark
  • Hands-on with Airflow for workflow orchestration
  • Familiarity with Snowflake or similar cloud data warehouses
  • Experience with AWS (S3, EMR, Glue)

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Categories

data science & analytics MLOps & AI Infrastructure

Skills

python sql spark airflow snowflake

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

data science & analytics MLOps & AI Infrastructure

Skills

python sql spark airflow snowflake docker aws

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