Talks

Part of Lenny Rachitsky’s “The AI-Native Product Manager” free workshop series on Maven. This Lightning Lesson teaches the Forward Deployed Engineer (FDE) mindset used by leading AI companies like Palantir, OpenAI, and Anduril. The session focuses on closing the gap between what teams build and what customers actually need — by embedding engineers in customer environments to observe real-world software usage. Register for free on Maven What You’ll Learn Build from real customer pain — Develop instincts by witnessing when systems fail in production environments Develop customer instinct systematically — A week-by-week playbook for customer calls, support shadowing, and feedback loops Avoid product-killing mistakes — Understand why teams overbuild unwanted features or underbuild critical functionality Background I built and led Project Frontline at Palantir, training 250+ engineers in the FDE methodology. Alumni now work at OpenAI, xAI, Anduril, and other AI-focused companies. I also led FDE practices at Citadel before co-founding Kepler. ...

Tell us about your background Throughout my career, I have dedicated myself to creating tools, products, and technologies that help people effectively utilize their data. My passion lies in developing products that enable users to efficiently and scalably gain maximum value from their data. My journey in understanding the intricacies of data and its potential began at Palantir Technologies, where I began working on search and indexing products. As data volumes grew, I focused my efforts on solving some of Palantir customers’ core problems across the financial and defense verticals before leading customer focused compute teams. After Palantir, I served as CTO at Veraset, a cloud-based data-as-a-service company. Veraset delivered high-quality, large scale data to a number of enterprises and grew to 15M ARR before being acquired. Following this, I joined Citadel Investment Group as the Head of Business Engineering of Ashler. In that role, I managed crucial data operations, including overseeing data pipelines, investment platforms, data lakes, and the software and data engineering teams responsible for them. ...

This week on The Data Stack Show, Eric and Kostas chat with Vinoo Ganesh. During the episode, Vinoo discusses how to benchmark cost, optimize your workloads, and Bluesky’s role in addressing your Snowflake bills. Video Link https://datastackshow.com/podcast/optimizing-query-workloads-and-your-snowflake-bill-with-vinoo-ganesh-of-bluesky-data/

Data is the most valuable resource in the world and more prized than oil, The Economist declared in 2017. Today, at least 97% of organizations use data to power their business opportunities, and we are accumulating data at a rate never before seen in history. The big question then is how do we secure and ensure that we can make optimal use of all this data? Link https://www.horangi.com/blog/s3-buckets-permissions-and-iam-audits

The data pipeline has become a fundamental component of the data science, data analyst, and data engineering workflow. Pipelines serve as the glue that links together various components of the data cleansing, data validation, and data transformation process. However, despite its importance to the data ecosystem, constructing the optimal data pipeline is generally an afterthought - if it’s considered at all. This makes any changes to the central pipeline highly error-prone and cumbersome. With the ever-growing demand for new kinds of data, especially from external vendors, constructing pipelines that are scalable and that allow for monitoring is pivotal for the safe and continued use of data. ...