About us

TVARIT GmbH specializes in developing and delivering cutting-edge artificial intelligence (AI) solutions for the metal industry, including steel, aluminum, copper, cast iron, and more. Our software products empower customers to make intelligent, data-driven decisions, driving advancements in Predictive Quality (PsQ), Predictive Maintenance (PdM), and Energy Consumption Reduction (PsE), etc. With a strong portfolio of renowned reference customers, state-of-the-art technology, a talented research team from prestigious universities, and recognition through esteemed awards such as the EU Horizon 2020 AI Prize, TVARIT is recognized as one of the most innovative AI companies in Germany and Europe.

Job description

As a senior data engineer, you will leverage your expertise in Azure Databricks, PySpark, and distributed computing to develop and optimize scalable ETL pipelines for manufacturing analytics. This role involves working with high-frequency industrial data to enable real-time and batch data processing, ensuring high-quality input for downstream analytics and collaborating with teams to provide structured data for AI/ML models.

• Build scalable real-time and batch processing workflows using Azure Databricks, PySpark, and Apache Spark
• Design and maintain cloud-based data architectures, including data lakes, lakehouses, and warehouses, following Medallion Architecture
• Deploy and optimize data solutions on Azure (preferred), AWS, or GCP with a focus on performance, security, and scalability
• Develop and optimize ETL/ELT pipelines for structured and unstructured data from IoT, MES, SCADA, LIMS, and ERP systems
• Automate data workflows using CI/CD and DevOps best practices, ensuring security and compliance with industry standards
• Monitor, troubleshoot, and enhance data pipelines for high availability and reliability
• Utilize Docker and Kubernetes for scalable data processing
• Perform data pre-processing, including cleaning, transformation, deduplication, normalization, encoding, and scaling to ensure high-quality input for downstream analytics

Relevant work experience

• 4+ years of experience in data engineering, with a strong focus on cloud platforms such as Azure (preferred), AWS, or GCP
• Experience in containerization (Docker, Kubernetes)
• Expertise in relational databases (e.g., SQL Server, PostgreSQL), time series databases (e.g., Influx DB), and NoSQL databases (e.g., MongoDB, Cassandra)
• Good to have MLOps, DevOps including model lifecycle management

Skills required for the job