Live opening · Posted 7 days ago
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Department Information
'EPC' is a department of BGSV company, belongs to Automotive Powertrain Solution domain. We develop embedded SW for 'Engine / Vehicle control unit' which controls key vehicle engine functions (e.g., power performance, energy consumption efficiency, emissions, diagnosis, communication) and additional drive-ability features.
As an Embedded Software Engineer in In-Vehicle Network Communication for European projects, you will join the dynamic development team across multi countries to develop in-vehicle network for Engine/ Vehicle control units.
Main Roles and Responsibilities
Develop and maintain embedded software for in-vehicle network communication in automotive system
Handle and support the requirement engineering include impact analysis
Perform static and dynamic testing from System level down-to SW component level using HiL / SiL environment:
System / System integration testing
SW / SW integration testing
SW component static analysis
Baseline & Variant management to ensure traceability between requirements, work products and test reports.
Participate in Problem identification & resolution (e.g., record anomaly found during testing, analyze, troubleshoot)
Report working status and escalate for barriers (to project manager)
Mandatory Qualifications
Bachelor degree in Electrical Electronics, Mechatronics, Computer Engineering or Relevant fields
Practical experience and knowledge about CAN, LIN, and FlexRay communication protocols.
Experience and knowledge about in-vehicle network communication in Automotive systems, and AUTOSAR COM
Experience with Automotive testing tools (e.g., CANoe, CANalyzer, VT System).
Proficiency in Embedded C programming language
SW development process: familiarity and practical experience with V-Model or Agile / Kanban methodologies in the context of Embedded development.
Nice-to-Have Qualification (Optional)
Knowledge about Ethernet communication protocol
Version Control & CI/CT: experience with Git, Git Actions, and Jenkins.
Practical experience to use AI effectively for research and self-study
Employment type
Full-time
Work arrangement
No
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