Das ist der Job
Rust proficiency (we use it for backend services and compute‑heavy client‑side modules). #J-18808-Ljbffr
Darum lohnt es sich
Share your workflows with the team Drive Continuous Improvement – Profile, benchmark, and tune Spark workloads, introduce best practices in orchestration & observability, and keep our tech stack future‑proof.
Own, Design, Build & Operate Data Pipelines – Take responsibility for our Spark-based pipeline, from development through production and monitoring. Advance our ML Models – Improve and productionise models for AdTech use‑cases such as lookalike modelling and demographics modeling.
AI‑Powered Productivity – Leverage LLM‑based code assistants, design generators, and test‑automation tools to move faster and raise the quality bar. Requirements Bachelor/Master/PhD in Computer Science, Data Engineering, or a related field and 5+ years of professional experience.
Expert‑level Python and PySpark/Scala Spark experience Proven track record building resilient, production‑grade data pipelines with rigorous data‑quality and validation checks. Data‑platform skills: operating Spark clusters, job schedulers, or orchestration frameworks (Airflow, Dagster, custom schedulers).
Working knowledge of ML lifecycle and model serving; familiarity with techniques for audience segmentation or look‑a‑like modelling is a big plus.
