Bruno Spricigo
Pre-analysis Engineer Navigation App @Pixida
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WORK HISTORY
Pre-analysis Engineer Navigation App @Pixida
Munich, DE
Analytical bridge between global testing teams and developers on BMW\'s Navigation App, covering defect triage, root cause analysis, and developer-readable reproduction steps across many active ECU generations (MGU18 through IDCEVO)- Built a personal engineering toolkit adopted by the team: ML defect classifier, test automation tool, Jira log pipeline, and a log flow pre-analyzer in development for Zuul CI integration- Manage team Jira Kanban board and custom filter architecture across a global defect pipeline (~10 defects/day from Germany, Brazil, Japan, US)- Cover several Navigation App subfunctions: Search, Speech, Parking, My Destinations, Destination Proposals, and backend/mobile integrations
EDUCATION
IFSC - Instituto Federal de Santa Catarina
Associate's Degree, Automotive Engineering Technology/Technician
Universidade Federal de Santa Catarina
Bachelor’s Degree, Bacharelado Interdisciplinar em Mobilidade - Veicular
Universidade Federal de Santa Catarina
Bachelor’s Degree, Automotive Engineering Technology/Technician
USP - Universidade de São Paulo
Master of Business Administration - MBA, Project Management
SKILLS
ABOUT BRUNO SPRICIGO
I\'ve been working in and around automotive software for over 10 years — starting with ADAS sensor R&D in Austria, moving through five years designing Industry 4.0 assembly lines for automotive and FMCG clients across Brazil and North America, and landing in Munich in 2023 as a Function Owner for BMW\'s Navigation App. Right now I work as a Pre-Analysis Engineer, which means I sit between global testing teams and developers — reading DLT logs, figuring out what\'s actually broken, and making sure whoever needs to fix it has everything they need to do it. I cover the full Navigation App across all active ECU generations, from MGU18 to IDCEVO. The thing I probably enjoy most is that I keep building tools to make the work less painful. Over the past year that turned into a small toolkit the team actually uses: an ML classifier that assigns incoming defects to the right Epics in Jira (~75% accuracy, saves around 50 minutes a day across the team), a test automation tool that connects to vehicles or racks via OBD, mirrors the screen, and captures DLT logs at the same time, and a bulk downloader that handles the whole attachment-unzip-filter pipeline from Jira tickets. I\'m also building a log flow pre-analyzer for Zuul CI integration, but that one\'s still cooking. None of it was asked of me. I just noticed the friction and did something about it. I hold a BSc. in Automotive Engineering, an MBA in Project Management, and a SAFe® 6 PO/PM certification — and I\'ve been writing Python long enough that it stopped feeling like coding and started feeling like thinking out loud. If you work in automotive software, embedded systems, or anywhere defect intelligence and process automation overlap — happy to connect.
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