We are looking for an experienced data professional to design and build a modern data pipeline for industrial IoT data in product development. In this project, you will work with a global machine manufacturer and solve how real-world usage data can be effectively utilized in engineering, simulation, and design.Role & ResponsibilitiesIn this role, you will design and implement a solution that connects machine-generated data with R&D analytics and simulation workflows.Your key objectives:

•  Design and implement an end-to-end data pipeline from field data (IoT + measurement data) to analytics 
•  Enable automated data processing and delivery for product development teams 
•  Build solutions that generate usage profiles and insights for simulation and engineering design 
•  Define and implement a scalable cloud-based data architecture
•  Integrate data engineering solutions with engineering and simulation tools
•  Collaborate with stakeholders to prioritize use cases and bring them into production

This is not just a pipeline project — it combines:

•  data engineering 
•  analytics 
•  and real-world product development needs 

RequirementsWe are looking for someone with strong experience in similar environments:Must-have skills

•  Proven experience in designing and building data pipelines (ETL/ELT)
•  Strong proficiency in Python (data processing, analytics) 
•  Experience with cloud platforms (e.g. Snowflake, Databricks, Azure, AWS) 
•  Solid understanding of data architectures (batch / streaming, data lake / warehouse) 
•  Experience working with scalable data processing systems

Nice to have

•  Experience with IoT or sensor data
•  Understanding of engineering or product development data
•  Experience with tools such as: 
  •  Snowflake 
  •  Databricks / PySpark 
•  Exposure to data analytics or machine learning
•  Ability to work in a client-facing role and facilitate technical discussions 

Expected OutcomeSuccess in this project means:

•  Data flows automatically from machines to analytics environments
•  R&D teams have access to clean, usable, and structured data
•  The solution is scalable and extendable to new use cases
•  Data can be directly utilized as input for simulation and product design