lessons from running engineering teams

Engineering leadership & career

Notes from leading teams: tech debt, institutional memory, and advice for staff engineers. I've led product and data teams at HealthVerity, Braided Technologies, and Bridge Connector, and I'm CTO at Clarity Health Project and fractional CTO at eSpiral Health. Here is what I learned the hard way.

All 11 articles

  1. 10 min readThe Two Rhythms of B2B Tech: What Palantir Gets Right That Most Companies Get WrongPalantir built a company on the idea that software alone isn't enough: you need engineers embedded with customers. That model has a name, a cost, and a hidden technical debt time bomb most B2B companies are quietly sitting on.
  2. 10 min readStaff Engineer Layoff Survival Guide: Lessons from 2008, 2020, 2023 — and NowI've survived three tech recessions. Lost my job in one, held the axe in another. The AI boom changed the rules again. The updated playbook for 2026.
  3. 9 min readThe Principal IC Playbook Nobody Shares With YouReaching principal is the first rung of a new ladder, not the last rung of the old one. What nobody told me about the ownership-autonomy paradox, how leverage works at this level, and the charter model I wish I'd had from day one.
  4. 7 min readThe Three Things Exceptional Engineering Leaders Do (And the One They Stop Doing)Most engineering leaders excel at one of three pillars and quietly fail at the other two. The three are: providing direction, removing obstacles, foreseeing change. What it takes to build strength across all three.
  5. 8 min readThree Ways to Know If Your Career Is Actually GrowingNormal career metrics: title, pay, team size. They tell you how you're doing relative to others. They don't tell you whether you're growing. Three metrics that do.
  6. 7 min readTime vs. Timing: The Career Framework I Wish I Had EarlierI've made bets that paid off because of timing and bets that paid off because of compounding. Confusing the two is how careers stall.
  7. 7 min readFrom Contractor to Consultant: The Mindset Shift That Changes Your IncomeContractors sell time. Consultants sell results. That distinction is the whole game. How I changed my pricing, positioning, and client selection once it clicked.
  8. 9 min readAgency Beats Intelligence: How I Now Hire (And Evaluate Myself)Raw intelligence is abundant and cheap. The engineers thriving in the AI era are the ones with agency: setting goals, acting under uncertainty, self-correcting. How I changed my interview process to find them.
  9. 8 min readThe Async-First Engineering Team: What Actually Works (And What Doesn't)I went async-first with my remote engineering team. Productivity went up. Culture took a hit. What changed, what broke, and the specific practices that made it worth it.
  10. 7 min readThe Death of Institutional Memory: Why I'm Building Atlas (And Why It Matters)Every resignation takes a piece of the organization nobody wrote down. Org charts capture hierarchy, not the relationship networks that keep things running. A different approach to preserving institutional knowledge before it walks out the door.
  11. 6 min readThe Food Truck Method: Building MVPs That Don't SuckMost MVPs turn into dumpster fires not because teams move too fast, but because they move fast in the wrong direction. The food truck method for building without the regret.

Common questions

What does a Palantir forward deployed engineer do?

An FDE is a software engineer who works at the customer site and writes production code against the customer's operational problems. Large contracts typically embed 3–8 FDEs for 6 to 18 months, building dashboards, workflow tools and AI-assisted decision apps in Foundry.

What is Palantir Foundry's ontology layer?

It is a typed model of the real world, a semantic graph where objects like suppliers, shipments and patients are nodes with properties and relationships. Foundry connects to existing systems such as Snowflake, SAP and Oracle and maps them into one object model without migrating the data.

Why does Palantir AIP depend on the ontology?

An LLM without grounding pattern-matches on tokens and will hallucinate about things like a supplier's contract dates. With the ontology, the model can query typed objects with live data attached, so it answers from real records.

What is a productization ritual in a B2B company?

It is a scheduled cadence with real ownership where the services team's custom work is reviewed for abstraction, and the viable pieces get rebuilt as reusable platform components. A quarterly business review where services reports customer requests to product does not do this job.