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# Senior Data Engineer - ML Platform

- **Company:** Sovendus GmbH
- **Location:** Frankfurt am Main, Germany (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Data Analysis, Apache HTTP Server, Microsoft Azure, Cloud Computing, Code Review, Information Engineering, Amazon DynamoDB, Apache Hive, Python (Programming Language), Machine Learning, Redis, Azure Machine Learning, SQL Databases, Sql Optimization, Apache Spark, Technical Debt, Data Layers, Data Lakes, Pyspark, Real Time Data, Apache Kafka, Machine Learning Operations, Terraform, Stream Processing
- **Published:** June 11, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=9451666e522f43ec

## About the Role

Do you have experience in Terraform?, * 5+ years of experience in data engineering or ML platform work in production environments

 * You write strong Python code, focused on production-grade applications (not just notebooks or scripts)
 * Advanced SQL (Athena / Spark SQL; SQL-first data transformations)
 * Kafka experience (producers, consumers, stream processing)
 * Solid Cloud experience (AWS preferred, or GCP/Azure)
 * Fluent English for daily collaboration within an international team.
 * Based in Spain with valid work authorization

It´s a plus if you have :

 * Spark & data lake ecosystem - Spark/PySpark (job structuring & tuning), plus lakehouse formats like Delta Lake or Iceberg
 * Orchestration & MLOps - Airflow, SageMaker or similar tools
 * ML data workflows - experience with feature pipelines, training datasets, and online serving

## Description

* Work in a modern, cloud-only AWS stack - EMR, EKS, S3, Athena/Glue, SageMaker, DynamoDB - orchestrated with Airflow and managed as code with Terraform, with a clear path to bringing every remaining piece fully onto this stack.
 * Shape the Kafka backbone that carries user-journey events from upstream services to the data lake, ML training, and analytics consumers.
 * Move data at scale with Spark SQL on Delta Lake and Apache Iceberg - ACID storage, time-travel, and concurrent-write reliability on top of S3.
 * Build the data layer for our recommendation engine - Athena schemas and views for model training, plus the real-time data assets (Redis, DynamoDB) that the recommendation service reads at request time.
 * Push AI-assisted development and operations further - we invest heavily in AI tooling for development, code review, documentation and operations, and we're actively deepening that. You'll help shape how the team works.
 * Steward the platform's long-term health - drive version upgrades, reduce tech debt, and keep the stack on the leading edge.


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