DTICI_DnA_LeadDataProductEngineer_T8

At Daimler Truck, we change today’s transportation and create real impact together. We are seeking a skilled Lead Data Product Engineer to define, design, build, and operate scalable Data Products for aftermarket uptime revolution . You will play a crucial role to unlock value in our data ecosystem by working in cross functional teams leveraging cloud data analytics technologies to enable data accessibility, quality, and insights across the organization.

ABOUT US At Daimler Truck, we change today’s transportation and create real impact together. We take responsibility around the globe and work together as one global team. We drive our progress and success together – everyone at Daimler Truck makes the difference. Together, we want to reduce our carbon footprint, increase safety on and off the track, develop smarter technology and attractive financial solutions. All essential, to fulfill our purpose - for all who keep the world moving. Become part of our global team: You make the difference - YOU MAKE US

Effective communication with stakeholders to define smarter and faster data driven solutions. 7+ years of Design & implementation of Azure Based Big Data Solutions – Datalake, Data Mart, Data Mesh Hands on experience in Hadoop, Spark, Databricks, ADX, Synapse and PySpark/Python. Proven ability to work in cross-functional agile teams. Ability to define Data Model, Design and Implement non functional requirements.

Design and implement the datalake/data mesh solutions for achieving Data Driven business process Consulting to design the Azure or Snowflake based Cloud data solutions Exploration of new technologies for faster & smarter data management as well as mentor the team. Work closely with data scientists, analysts, and other stakeholders to enable data-driven projects and provide access to reliable, well-structured data. Data Modelling: Develop and manage data models within Databricks/Snowflake, ensuring efficient, secure data organization and accessibility. Data Pipeline Development: Design, build, and optimize data pipelines to ingest, transform and load data from multiple sources, using Azure Databricks/Snowflake. Data Transformation: Implement transformations, standardizing data for analysis and reporting. Performance Optimization: Monitor and optimize pipeline performance, troubleshooting and resolving issues as needed.