Marcellus Pelcher

When Steve Huynh was a principal engineer at Amazon, meetings began with a "study hall." Amazon had a "reading culture" even among engineers, Huynh recently told the Pragmatic Engineer podcast, speaking of his time at the tech giant. Employees frequently drafted six-page memos, which they shared with the company to update progress and demonstrate new projects. Huynh, who said the company's embrace of writing and reading the memos was part of its "secret sauce," said employees' writing was often constrained to the format, whether it was a business strategy or press release. Huynh started at Amazon in 2006, only a few years after the company turned its first profit and while Jeff Bezos was at the helm. Read more about how Bezos instilled this culture of memo-writing from the top down on Business Insider: https://lnkd.in/gBqbEf_j (Credit: Daniel Berman) #amazon #reading #jeffbezos

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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Our friends over at Discord incredible engineering blog on how they're processing petabytes of data with dbt Labs and Dagster while supporting 100+ developers working across 2,500+ models! Key Highlights: 📦 Environment separation strategy to prevent developers from overwriting each other's test tables 📈 Performance optimizations that achieved a 5x speedup in dbt execution times 🧠 Smart versioning system for automated, efficient backfills ⚙️ Comprehensive macros and CI/CD guardrails for data quality Great work Chris D. ! Link in the comments.

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The biggest difference between Google and Waymo, almost one year in: Waymo has largely adopted the Amazonian practice of presenting information in documents, not slides. Meetings start with a few minutes of pre-read / frantic commenting on a Google Doc, followed by a live burn-down of the comments that require discussion. It's efficient and information-dense. I love it.

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From the start, Metronome knew real-time billing couldn’t be built on batch. That’s why they chose Confluent as the foundation for their data architecture, streaming billions of events daily and processing tens of thousands of invoices per second with ease. We handle the infrastructure complexity so 90% of their engineers can focus on building and improving the product. Explore how they’re delivering a modern billing experience powered by real-time data infrastructure → https://cnfl.io/40S0YDS

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We just released Taro's newest course about what life is really like at FAANG. So much of the discussion about working at FAANG focuses on things like the scale of the work or the free snacks. But on the day-to-day level, an engineer at Google or Facebook doesn't think about these topics. They are spending most of their waking hours on things like: - how much scope do they have? - how much control do they have vs just being a cog in the machine - what do their coworkers think about them? As a 5 year veteran of Meta, I break down the real pros and cons of working in Big Tech. Check it out here: https://lnkd.in/gfkwVDf9

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“In a very binary sense, Metronome made self-service at Anyscale possible.” That’s Matt Weber, Software Engineer, reflecting on Anyscale's expansion into new revenue channels. As the team added PLG and cloud marketplaces (AWS, GCP, and Azure), their manual billing setup couldn’t support the volume or complexity. Reporting, reconciliation, and manual workflows were slowing them down. With Metronome as their billing foundation, Anyscale unified every revenue motion on one platform and accelerated growth across the business. They launched self-serve billing end-to-end, brought AWS, GCP, and Azure marketplaces live in under three weeks each, and freed up three engineers and a PM from dedicated billing work. 🎉🤝 Big congratulations to the Anyscale team on the expansion—this is what it looks like when innovative product and modern monetization infrastructure come together. Read the full story at the link in the comments.

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What the heck are Quorums? Quorums are pretty important when it comes to distributed systems, but what are they all about, and why do they matter so much? Continuing my series dissecting the Amazon Aurora paper, I just published the video covering section 2 of it, which covers Durability using Quorums and some of their key design decisions. give it a watch - youtu.be/EaWlpn24eAs The first video of the series is not a pre-req for this one, so give it a watch and understand what Quorums are all about and how databases leverages them to offer high durability. All the key links regarding the series are in the comments down below.

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Kikoff is constantly innovating to build smarter, faster financial tools. In an interview with Built In SF, Engineering Manager Patrick Wang shares how the team is tackling technical debt and optimizing performance to solve problems for customers, faster. Read more on what’s ahead: https://lnkd.in/gBvk7cQ9

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Joakim Recht (Uber Distinguished Eng) on tech leads: "Software engineers need to write code. If you're not writing code, you're not a software engineer." Joakim Recht grew from a Senior Engineer to a Distinguished Engineer at Uber and I asked him what it took to get there. We covered: • The project that grew his career • What makes a great software engineer • Learnings from promo committees • When promos are unfair • Biggest technical mistake at Uber in hindsight • Advice for his younger self 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 𝗹𝗶𝗻𝗸𝘀: • YouTube: https://lnkd.in/gTvsB3Ar • Transcript: https://lnkd.in/gG7bmeU2 • Spotify: https://lnkd.in/g4n-fUxe • Apple: https://lnkd.in/gm8r-2_R

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Excited to announce Ray on Anyscale is now available on CoreWeave to further deliver on our multi-cloud support. This gives users access to CoreWeave’s purpose-built AI infrastructure, optimized for distributed AI at scale with low latency, fast startup times, and high performance GPUs. It's a powerful combination for teams building and scaling production AI workloads. Read more from CoreWeave: https://lnkd.in/dN9AjA3s

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