Day 1 of fixing my brain after the worst depressive episode I’ve had in 15 years. Brains need the body to be satisfied before they allow happiness. This is why so many people say “just move more and eat better” and then you’ll be happy. This ignores the fact that depression makes those two things extremely difficult. This week I am starting ketamine therapy for treatment-resistant depression. Sometimes your car needs a jump start to break the bad patterns. I’m hopeful that this will make my life better. My goals to “win the day” are much lower than they used to be: - walk for at least 30 minutes - talk to one person who makes me smile - eat 3 meals per day It’s intense what happens to your nervous system when the old stories you used to tell yourself don’t provide it fuel anymore. When your old meaning gets obliterated, it definitely makes it harder to get up in the morning. My goal is to be 20% less depressed by January 2027. This is empirically measured by the following things: - reduction in screen time - reduction in resting heart rate - increase in socializing time - increase in outside time If this works out, amazing. If not, at least I can say I tried.

Stop identifying with your job title! It’s a trap that will keep your poor You aren’t a data engineer or a product manager. You’re a human capable of adapting and learning so much. When I was working at Airbnb, they were like, you’re one of the best data engineers, Zach. We want you to become a principal data engineer! The glory of being a principal engineer in big tech. Wow! And I drank the Koolaid fully from 2016 to 2023. But so much sugar ultimately made my stomach sick and I realized my life was more than SQL, Scala, Spark and Airflow. I irrationally left a $600k data engineering job for a $0 content creator business. I was destined to be an internet clown and educator. I traded SQL for shitposting. I traded Spark for sales. I traded Scala for scaling my business. I traded Airflow for art. And now this absolutely absurd decision in 2023 is looking better and better as technical skills continue to get undermined by AI. Give yourself to reinvent yourself! Give yourself permission to change jobs! You’re a multi potential human Intelligence is the ability to adapt to change!

Kevin O'Leary is destroying my home town in Utah for the sake of corporate greed! He’s pushing to build a data center near my hometown in Utah that uses 558 MILLION gallons of water every single year. Utah is the second driest state in the US and DOES NOT HAVE the water to support this data center. This plan will rapidly dry up the Great Salt Lake and we will have a very similar catastrophe like what happened to the Aral Sea in Kazakhstan At the bottom of the Great Salt Lake is tons of arsenic that will be exposed if this project gets approved! The protests a few months back caused him to decline building the data center. But he REFILED to build it. We as Utahns MUST FIGHT BACK! If you want to be part of the protest. His water application number is a55167 and you can submit a formal protest for $15. If you’re an Utah resident, I will send you $15 to submit the formal complaint (just send me a screenshot of your submitted application in a LinkedIn DM and your Venmo handle) You can submit your formal complaint here: waterrights.utah.gov/protest… Get out of Utah and go back to Canada, Kevin! Your data center that will poison the children in my home town for generations! Please unfollow Kevin. He will stomp on Utah children to get as much profit as he possible can!

Turning a vibe-coded data pipeline into production-grade software requires thinking about 100s of different engineering concepts! AI can generate an ETL pipeline in minutes. But getting that pipeline to process billions of records reliably, maintain data quality, and recover from failures? That’s a completely different animal! Here’s what vibe coders building data pipelines need to learn: 1. Data Ingestion & Extraction * Batch vs. streaming ingestion * Change Data Capture (CDC) * Full vs. incremental loads * API rate limiting * Schema inference * Data serialization * Idempotent ingestion * Late-arriving data 2. Data Modeling & Storage * Star schemas * Snowflake schemas * Slowly Changing Dimensions (SCDs) * Fact and dimension tables * Data normalization * Partitioning * Clustering * File compaction * Columnar storage * Data lakehouse architectures 3. Distributed Data Processing * Data shuffling * Data skew * Broadcast joins * Sort-merge joins * Predicate pushdown * Partition pruning * Parallelism * Memory management * Adaptive query execution 4. Orchestration & Reliability * DAG orchestration * Task dependencies * Retries * Exponential backoff * Idempotency * Backfills * SLAs * Data freshness * Failure recovery * Workflow scheduling 5. Streaming & Real-Time Data * Event time vs. processing time * Watermarks * Windowing * Exactly-once vs. at-least-once processing * Event deduplication * Out-of-order events * Checkpointing * State management * Kafka partitions * Backpressure 6. Data Quality & Governance * Data contracts * Schema evolution * Schema enforcement * Null handling * Duplicate detection * Referential integrity * Data validation * Data lineage * PII handling * Access controls * GDPR compliance 7. Performance & Cost Optimization * Warehouse sizing * Autoscaling * Query caching * Materialized views * Incremental materialization * Storage optimization * Compute optimization * Cost attribution * Concurrency management * Query profiling 8. Deployment & Observability * CI/CD for data pipelines * Infrastructure as Code * Environment isolation * Unit testing * Integration testing * Data reconciliation * Pipeline monitoring * Data quality alerts * Freshness monitoring AI has reduced the cost of writing data transformations! But it hasn’t eliminated the complexity of running data pipelines at scale. You can easily vibe code your way to a pipeline that works on 10,000 rows. But when that pipeline needs to process 10 billion rows, handle schema changes, recover from partial failures, or guarantee downstream data correctness. You’re basically cooked！ The future isn’t data engineers vs. vibe coders. It’s data engineers who understand distributed systems, data architecture, and production reliability AND know how to leverage AI. Writing SQL and Python is getting cheaper. Data engineering judgment is becoming more valuable！ What would you add to the list? Inspired by Natalie L.

Job hopping in 2026 isn’t like what it was in 2021! Oprah isn’t out there giving everybody jobs like during the zero interest rate world That being said, job hopping is still one of the best ways to get promoted and raises - the new company judges you based on your stories and potential - the current company judges you based on your results Sometimes being too results focused can cause management to be too conservative in their promotion criteria. Managers all have a story of promoting someone too early and regretting it but they don’t have as many stories of regret where they make an engineer wait. If you believe you’re at the next level, the best way to test that is by going out into the market and trying! But in a layoff heavy environment, the new hires are often the ones most likely to get laid off. So it’s not as clear cut as it was in 2021. I’m doing a one hour webinar with Prasad Rao on how to determine if job hopping is right for you or if getting promoted within your company is better! You can join here: bigtechcareers.techexpert.io…

The layoffs from Netflix are really hard to see! I’ve always held Netflix to a higher standard. They keep teams smaller intentionally. They have high standards of their employees and let go people who truly aren’t A players. Seeing them let go 1,000 A players today shows me that Netflix is in a tough spot. Talent is what has made Netflix great! If you were affected by these layoffs, we can get you 50% off the DataExpert platform so you can upskill and maybe get you a job at Anthropic or OpenAI like Mike Chuang (one of our previous students). Send me a DM and we can schedule a free 1:1 so I can gas you up and show you these layoffs will be the greatest thing to ever happen to you!

If I move to SF in January, it will be my sixth time moving to SF since 2018. Here’s a summary of the six other times of why I moved to SF. - August 2018 One of my best friends suggested I move to SF and be her roommate. After living in Fremont for a year, SF was way better! I did that and wow my life changed. This did increase my commute from 20 mins one way to 90 minutes one way though. - October 2019 The commute to Netflix broke me and I moved to San Jose in August 2019. I hated San Jose with a fiery passion. The soul is lost in that city so I moved back to SF in October 2019. I realized a few months later I needed to quit my job at Netflix. - August 2021 The pandemic response in SF broke my mental health pretty bad. So I left in January 2021 and moved back to Utah. Utah reminded me pretty quickly why I moved out in 2016 and I moved back to Cali seven months later. - February 2023 After rage quitting SF again in October 2022, I decided to move to Denver. Denver was just so painfully boring. Then in January 2023, I did a creator house in Utah with Ken Jee and Tina Huang. Tina convinced me to move back to SF and reclaim my power and become a full time creator. - January 2025 After burning out as an entrepreneur in July 2024, I left SF to do creator houses with MadelineMadeline Zhang, Arin Goldsmith, Fritz Johnson, Carly Taylor, and Annie Nelson. I thought I discovered a new way of living. But being untethered ultimately taxed me more than I realized. - January 2027 I left California in January 2026 after dozens of VC rejections and realizing I can run my business anywhere. I traveled and saw a dozen countries. I became a Nevada resident. I shifted my business to be more subscription oriented to give me more time back. Maybe SF is the right place? I’ve moved to SF more in 8 years than most of you have moved in your entire life!

Every time I start focusing on early retirement, I get depressed. I hit my FIRE number to never work again back in January of this year. This was followed by one of the most soul-crushing depression feelings I have ever had in my life. I realized I had organized my entire life around achievement for survival. And once survival was taken care of, achievement started to not lift me up the same way it did for the last ten years. This caused me to uncover the most uncomfortable belief that many men in America feel, I equated my productivity with my worth. So every step towards early retirement was a step towards worthlessness. And wow, realizing the destination I was yearning for since I was 21 was actually a state of worthlessness shook my entire body. So, I decided to travel and see the world. I saw Finland, Sweden, Norway, Denmark, India, Thailand, and Taiwan. I came back from my travels, and I was still depressed. Nothing had actually changed. My productivity and worth were still inextricably linked. So, is there a way to contribute while also being retired? Or do you just put your head in the sand and move back to San Francisco? 😂

Things I refuse to accept in my 30s: - back pain Do yoga every day for 20 minutes and your back pain will disappear. Flexibility is the most underrated form of fitness. - being sedentary 95% of 30 something’s will never sprint again in their life. I make a habit to sprint EVERY DAY. Even if it's only for 5 minutes. Can you run as fast as your body will let you for 5 minutes? - gaining weight This is easily solved by not eating junk food. Eat good food like salmon, eggs, fruits and veggies. Be mindful of the quality of the food you eat and everything else will sort itself out. - losing important friendships Check in on your friends. Make sure you don’t let busy become your new form of identity that they forget that you care. These are my guiding lights as I enter into the third year of my 30s

Analytics Engineering has levels to it. Level 1: Writing queries Start by mastering the basics: •Strong SQL fundamentals including CTEs, window functions, and subqueries •Querying modern warehouses like Snowflake, BigQuery, Redshift, and Databricks •Understanding table grain, joins, and how nulls behave •Using BI tools such as Looker, Tableau, Power BI, Mode, or Metabase •Light transformations in SQL or Python At this stage, you are focused on answering questions and building confidence in the data. Level 2: Modeling analytics data Move from querying data to shaping it: •Dimensional modeling with facts, dimensions, and star schemas •dbt fundamentals including models, tests, sources, and documentation •Incremental models, snapshots, and slowly changing dimensions •Handling late arriving data and backfills •Basic orchestration with tools like dbt Cloud, Airflow, or Prefect This is where analytics becomes repeatable and reusable across teams. Level 3: Engineering the analytics layer Shift from individual models to systems: •Clear analytics layers from staging to marts •Consistent metric definitions and shared business logic •Version control, code reviews, and CI for analytics projects •Data quality checks using dbt tests or tools like Elementary and Monte Carlo •Environment separation and safe deployments •Reverse ETL with tools like Hightouch or Census Here is where you move from “it works” to “it keeps working.” Level 4: Operating analytics at scale Learn what it takes to support analytics in real companies: •Semantic layers and metric governance with Looker, dbt Semantic Layer, or Cube •Performance tuning with partitioning, clustering, and cost controls •Data observability and incident response using tools like Monte Carlo or Datafold •Data contracts, schema enforcement, and lineage •Access controls, PII handling, and compliance •Tight collaboration with product, finance, and leadership At this point, you are not just building dashboards. You are running the decision making layer of the company. Optional Level 5: Analytics as a platform Not every team needs this, but larger companies do: •Self serve analytics and enablement •Metric APIs and shared data products •Embedded analytics in customer facing products •Company wide analytics standards and education Most teams think they are at Level 3. Most are actually stuck between Level 1 and Level 2. What would you add or change based on your experience?