Based in Sydney, Australia, Foundry is a blog by Rebecca Thao. Her posts explore modern architecture through photos and quotes by influential architects, engineers, and artists.

Episode 58 - Using Reflection from Media to Engineering with Mark Weiss

Episode 58 - Using Reflection from Media to Engineering with Mark Weiss

 
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Today's interview is with Mark Weiss, Data Engineer at Beeswax and host of Using Reflection which is a podcast where engineers talk about what they love and what they've learned in their careers. I asked him about what he personally has learned from his interviews and from his own career started in the news media.

Then, we turn to the craft of data engineering - on which the viability of all the internet services you love (or hate) hinge upon. There is so much change going on in the data engineering world, and I asked Mark about that change, and where he hopes and thinks it can go in the future.

About Mark Weiss

Mark Weiss is a Senior Software Engineer at Beeswax, an online advertising buying platform based in New York City. He has previously held various engineering individual contributor and leadership roles, and has worked on ETL systems and data-driven distributed platforms for much of his career. Mark has spoken at DataEngConf NYC and at the NYC Python Meetup. He is also blogs and hosts the podcast "Using
Reflection" at usingreflection.com, and can be found on Github and LinkedIn under marksweiss and on Twitter @marksweiss and @UsingReflection. He lives in Brooklyn, NY.

Related Episodes

Episode 34 with Joe Crobak on Data Engineering
Episode 39 on Well-Foundedness

Mentioned Episodes on Using Reflection

Hayden Cacace on Life and Gaming
Andrew Marsh on Problems with No Right Answers
Jenny Young on the Brooklyn Robot Foundry
Aaron Boyd on Engineering Management

Additional Links

Anthony Molinaro’s SQL Cookbook

Episode 59 - Internet in Cuba, Bitcoin in Venezuela

Episode 59 - Internet in Cuba, Bitcoin in Venezuela

Episode 57 - Data Science, Analogies, and Nearest Neighbors

Episode 57 - Data Science, Analogies, and Nearest Neighbors