Senior Data Engineer

About TVARIT

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. We are seeking a self-motivated individual with a positive "can-do" attitude and excellent oral and written communication skills in English to join our team.

Job Description:

We are looking for a Senior Data Engineer with strong expertise in Azure Databricks, PySpark, and distributed computing to develop and optimize scalable ETL pipelines for manufacturing analytics. The role involves working with high-frequency industrial data to enable real-time and batch data processing

Key Responsibilities · Build scalable real-time and batch processing workflows using Azure Databricks, PySpark, and Apache Spark.

· Perform data pre-processing, including cleaning, transformation, deduplication, normalization, encoding, and scaling to ensure high-quality input for downstream analytics.

· 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.

· Collaborate with automation team, data scientists and engineers to provide clean, structured data for AI/ML models.

Desired Skills and Qualifications · Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.

· 4+ years of experience in data engineering, with a strong focus on cloud platforms such as Azure (preferred), AWS, or GCP · Proficiency in PySpark, Azure Databricks, Python and Apache Spark, etc.

· Expertise in relational databases (e.g., SQL Server, PostgreSQL), time series databases (e.g. Influx DB), and NoSQL databases (e.g., MongoDB, Cassandra) · Experience in containerization (Docker, Kubernetes).

· Strong analytical and problem-solving skills with attention to detail.

· Good to have MLOps, DevOps including model lifecycle management

· Excellent communication and collaboration skills, with a proven ability to work effectively as a team player.

· Comfortable working in a dynamic, fast-paced startup environment, adapting quickly to changing priorities and responsibilities.