Podcast
Transition from QA to Machine Learning & Data Engineering: Projects, Cloud & Interview Prep
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Transition from QA to Machine Learning & Data Engineering: Projects, Cloud & Interview Prep
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Episode Overview
How do you move from a QA role into machine learning and data engineering—what projects, cloud skills, and interview prep actually make a difference? In this episode Alvaro Navas Peire walks through his journey from testing Android phones and QA checklists to quitting the industry, taking a gap year, and retraining in machine learning and data engineering. With an informatics engineering background and hands-on experience from postgraduate courses, Neuromatch, and DataTalks’ ML & DE Zoomcamps, Alvaro explains the.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:15 - Early Life & Informatics Engineering; phone industry beginnings
- 3:41 - Phone prototyping and field testing: QA checklists, CTS & RF testing
- 8:35 - Career pivot: quitting QA, gap year, and discovering machine learning
- 13:32 - Structured learning path: postgraduate course, Neuromatch Academy, ML & Data
- 17:57 - Job search strategy: improving soft skills, hiring a coach, and CV redesign
- 22:38 - Interview soft skills: role-play, confidence building, and behavioral prep
- 24:57 - Zoomcamp projects: speed-dating EDA and vegetable image-classification
- 27:16 - Project deployment experience: Google Cloud, AWS exercises, and cloud credits
- 28:52 - Presenting projects objectively: avoid underselling and focus on facts
- 31:38 - Interview formats encountered: take-home tasks, time-series exercise, and
- 34:01 - Cloud familiarity in interviews: Google Cloud, Azure, AWS—what mattered
- 35:02 - Creating technical notes: long-form Markdown, GitHub gists, and screenshots
- 37:18 - Note-taking workflow: video pause-write method, indexes, and VS Code
- 43:33 - Role of a coach: negotiation practice, interview framing, and communication
- 47:39 - Skill distinction: math-heavy research ML vs. tooling-focused data engineering
- 49:32 - Technical interview prep: tailor study to role, projects, and hands-on exercises
- 51:53 - Typical workday as an ML project manager: planning, Teams, and task coordination
- 54:12 - Production tech stack: Azure, Databricks, AutoKeras, Azure Data Factory,
- 56:11 - Transition advice: programming background, math, and transferable skills
- 1:00:26 - CV and portfolio tips: visual résumé, GitHub visibility, and sample CV link