Informacje o stanowisku

At Grape Up, we transform businesses by unlocking the potential of AI and data through innovative software solutions.

We partner with industry leaders, from the automotive and finance industry, to build sophisticated Data & Analytics platforms that transform how organizations manage and leverage their data assets. Our solutions provide comprehensive capabilities spanning data storage, management, advanced analytics, machine learning, and AI, enabling enterprises to accelerate innovation and make data-driven decisions.

• Master’s degree in computer science, Data Engineering, AI, or a related field
• 4+ years of professional experience in Data Engineering and Big Data, building production-grade data platforms and pipelines
• Proven experience with Databricks platform (Azure Databricks or Databricks on AWS)
• Strong experience with Apache Spark (PySpark), including performance optimization, and large-scale data processing
• Proficiency in Python and SQL for data transformations and analytics workloads
• Experience with data pipeline orchestration (Airflow, Dagster, Prefect or similar)
• Experience with data governance and quality frameworks
• Strong problem-solving skills and ability to work independently
• Fluency in English, both written and spoken

At Grape Up, we transform businesses by unlocking the potential of AI and data through innovative software solutions.

We partner with industry leaders, from the automotive and finance industry, to build sophisticated Data & Analytics platforms that transform how organizations manage and leverage their data assets. Our solutions provide comprehensive capabilities spanning data storage, management, advanced analytics, machine learning, and AI, enabling enterprises to accelerate innovation and make data-driven decisions.

,[Implement a scalable architecture capable of handling the high volume of simulation data, Build a flexible data preprocessing pipelines that are extensible and that can be integrated into customer’s existing platform, Define KPIs to measure the improved reusability and automation of the new pipelines and test their performance in an end-to-end setting with model training, Develop and implement processes and best practices for data management and governance, Optimize and enhance system setup and improve data structures following industry best practices, Collaborate effectively with data engineering team members while partnering closely with analytics and data science teams to meet user needs, Collaborate with business stakeholders and technical teams to understand data requirements, translate business needs into technical solutions, Lead technical discussions and solution design sessions with clients or internal stakeholders, presenting complex data engineering concepts in accessible ways Requirements: Python, SQL, AWS, Big data, Distributed computing, Spark, Data pipelines, Data engineering, Airflow, Databricks, PySpark, Dagster, Prefect, Terraform, CloudFormation, Kubernetes, Machine learning, MLOps, Unity Catalog, Delta Lake, Kafka, Azure Event Hubs Tools: . Additionally: Private healthcare, Flat structure, International projects, Training budget, Small teams, English lessons, German lessons, Internal tech talks, Integration events, Internal trainings, Free coffee, Bike parking, Playroom, Shower, Free snacks, Free parking, In-house trainings, Modern office, Startup atmosphere, No dress code, In-house hack days, Free beverages.

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