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How I Landed a Data Engineering Job: Bootcamp, Docker, Airflow, AWS & Interview Tips
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How I Landed a Data Engineering Job: Bootcamp, Docker, Airflow, AWS & Interview Tips
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Episode Overview
How do you go from neuroscience research to a data engineering role — and what practical steps and skills actually get you hired? In this episode, Gloria Quiceno, Senior Analytics Engineer at ICE, walks through her transition from neuroscience labs to rebuilding enterprise data platforms (including a BI rebuild that saved €250K), and the concrete tools and tactics that landed her a data engineering job. We cover her early lab automation and scripting experience, learning MATLAB/R and Python, a first industry role.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:58 - Background: Transition from Neuroscience Research to Industry
- 2:41 - Lab Automation & Scripting: Igor, C-style Code and Data Collection
- 6:33 - Learning Curve: MATLAB, R and Falling in Love with Programming
- 7:46 - First Industry Role: Business Data Analyst Duties and SQL Reporting
- 11:53 - Company Overview: Music Metadata, Royalties and Data Workflows
- 16:14 - Job Search Timeline: Bootcamp Graduation to Offer in Four Months
- 18:21 - Volunteer Experience: Omdena Project for Practical ML Practice
- 19:52 - Role Focus: Choosing Data Engineering Tasks and Automation Work
- 21:25 - Reproducibility: Docker for Collaborative Scripts and AWS Runs
- 22:57 - Application Strategy: Tracking ~130 Applications and Organization
- 27:55 - Interview Hurdles: Live Coding Pressure and Take-Home Challenges
- 30:19 - Applicant Rights: GDPR and Deleting Recruitment Records
- 31:29 - Salary Negotiation: Market Research, Confidence and Tactics
- 36:20 - Bootcamp ROI: Learning Python, Docker, Airflow and Networking
- 37:25 - Retrospective Tips: Earlier Career Coaching and More Networking
- 45:29 - Cloud Platforms: AWS vs GCP Experience and Managing Credits
- 50:15 - Capstone Project: Twitter Data Pipeline, Docker Containers and Slack Bot
- 51:42 - Portfolio Strategy: Custom Projects to Stand Out to Employers
- 53:34 - Data Quality: Detecting Bots, Cleaning Twitter Data and Sentiment Bias
- 56:48 - Cohort Diversity: Varied Backgrounds and Cross-disciplinary Strengths