The Joe Reis Show

I get asked by junior-level graduates and candidates in data engineering, analytics, and data science about what to do to succeed in today's challenging job market. With AI rapidly changing employer expectations, there's understandably a lot of anxiety today, particularly among juniors.In this Freestyle Friday, I answer in terms of what I'd do if I was a junior candidate today - the necessary tech and personal skills, my top 5 traits in a candidate, and building one's network.Obviously I can only speak from my perspective, but hopefully there's some helpful advice here.-----------------------Sponsor: Revefi Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure.With a five-minute, zero-touch setup, it deploys 18 specialized agents across your data estate to automatically manage FinOps, performance tuning, and data quality. If you want to cut your cloud costs by 30% to 70% and get back to actual data architecture, check out what they are building at revefi.com/ai-dba

Maxime Beauchemin, the creator of Apache Airflow and Apache Superset, joins the show to discuss his transition from data engineering to the frontier of AI. Max shares the origin stories of his massive open-source projects, detailing how Airflow was born at Airbnb out of a need for better data orchestration. He also explains his shift toward user interfaces with Superset and the founding of his company, Preset. We then shift to the future of software development. Max dives deep into his recent "Claude Code moment" and introduces Agor, his new command center for agentic workflows. Discover how his team is using persistent AI agents to automate everything from deal desks and legal reviews to automated bug bashing and QA testing. Finally, the discussion explores the tech singularity, the concept of a "software utopia," and how awesome hobbies like mountain biking and e-foiling will fit into an automated world.Agor: https://agor.live/

Finished a jog on the high plains near South Pass, Wyoming, where there's a lot of history. Standing near the old Oregon and Mormon trails, I look at the remnants of the 1800s gold rush to draw parallels to the current AI boom.

In this episode, I sit down with Prukalpa Sankar, the founder of Atlan, to discuss the missing piece that makes artificial intelligence actually useful in the enterprise: context. We dive deep into building the "second brain" of a company, the reality of agent development, and how to transition a traditional business into an AI-native organization. If you're looking to understand why your AI agents are getting abandoned in testing hell or how the roles of data and engineering are fundamentally shifting, this is the conversation for you. As always, we keep it practical and grounded. No hype, just education from the front lines of data architecture.What an Enterprise Context Layer Actually Is (Prukalpa's new article): https://www.linkedin.com/pulse/what-enterprise-context-layer-actually-prukalpa--avdqc/?trackingId=kq8lIdYdRnKsHu%2BdREYB3Q%3D%3DTimestamps01:15 - The missing half of AI: Contextual intelligence 02:15 - Reverse engineering business context and the second brain 05:06 - Escaping testing hell and hitting the 80% accuracy threshold for agents 07:54 - Simulating context for analytics use cases 11:34 - Does data quality matter for AI agents? 15:37 - Capturing tacit knowledge and human expertise 21:08 - The organizational chart of the future and "E-shaped" humans 26:26 - How Atlan transformed into a completely AI-native company 34:22 - Banning engineers from coding and the new mental model for work 39:05 - Societal resistance, historical context, and embracing technological change 46:00 - Optimism, childlike curiosity, and the path forward

Juan Sequeda stops by after a massive month on the road to unpack the latest industry shifts, including takeaways from the Snowflake Summit. We dive into the real state of AI agents in the enterprise, separating the hype from the reality of adoption. We also explore the dangers of creating a "semantic swamp," (cousin of data swamps) the shifting landscape of vendor strategies with the rise of the modern monolith, and why data teams need to accept that getting work done (not data) is the true center of the universe. Finally, we discuss why pragmatism beats pedantry every time when building data architecture.🎙️ SPONSORSRevify - surprise Snowflake bills? One customer cut theirs 50% in 48 hours.→ https://revify.com/demo