Podcast
Build a Data Engineering Career: Bootcamp Curriculum, SQL Mastery & Interview Prep
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Build a Data Engineering Career: Bootcamp Curriculum, SQL Mastery & Interview Prep
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Episode Overview
How do you build a data engineering career from zero — what should you learn, how do you master SQL, and how do you pass the interviews? In this episode, Jeff Katz — former lawyer turned developer, founder of Jigsaw Labs, and current ML engineer at AppFolio — walks through practical paths into data engineering and how to design bootcamp curriculum that actually leads to hires.
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Chapter Summary
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- 0:00 - Episode Overview & Guest Introduction
- 1:20 - Guest Background: Lawyer → Developer → Educator
- 3:56 - Active Learning & Continuous Student Feedback (teaching methods)
- 6:32 - Education as Social Impact: Training, Refugees, Last-mile
- 8:42 - Early Bootcamps: General Assembly and Flatiron School Origins
- 9:58 - Curriculum Development: Market Research & Employer Validation
- 11:44 - Lesson Structure: Syllabi, Labs, Reinforcement Cycles
- 14:30 - Pedagogy: Conceptual Understanding Before Implementation
- 15:24 - Market Shift: Why Data Science Moved Toward Data Engineering
- 16:58 - Building a School: Affordability, Part-time Model, Career Services
- 20:18 - Lowering Barriers: Workshops, Part-time Pathways, Admissions
- 23:35 - Data Engineering Core Skills: Python, SQL, Cloud Fundamentals
- 26:40 - Ensuring Hires: Admissions Criteria, Curriculum-Employer Fit, Follow-up
- 27:41 - Mid-Program Internships: Employer Projects for Real Experience
- 30:32 - Applicant Screening: Technical Interview & Learning Agility
- 33:05 - Interview Practice: Apply Early, Learn from Rejection
- 36:18 - Analytics Engineering Module: DBT, Snowflake, Mode, Fivetran
- 37:41 - Backend Engineering Module: Flask, ETL, Codebase Navigation, Testing
- 38:05 - Curriculum Prioritization: Dropping Spark/Kafka/Kubernetes for Juniors
- 40:42 - Transition Path: Data Analyst → Data Engineer (backend & cloud focus)
- 44:21 - SQL Mastery: Window Functions & Medium LeetCode SQL Problems
- 45:14 - Data Modeling Practice: OLTP vs OLAP and Sample Databases
- 48:00 - Interview Stages: Screening Calls, SQL Tests, On-site Expectations
- 49:52 - How to Start Teaching: Pick a Beginner Topic & Teach One Person
- 51:56 - Delivery Tactics: In-Person vs Online Engagement and Sequencing
- 54:54 - Running a Small School: Curriculum Volume and Time Management
- 56:46 - Teaching Fundamentals vs Shiny Tech: 85% Python/SQL, 15% tools
- 59:31 - Outcomes & Next Cohort: JigsawLabs Results and Start Date
- 1:00:21 - Contact & Follow-up: Jeff Katz, Webinar on Getting Data Engineering Jobs