New Guide for Aspiring Engineering Managers
Leanpub has launched "Engineering Manager’s Compass," a new book by Dunya Kirkali and Maxim Schepelin. The guide is aimed at individual contributors transitioning into management roles. It covers topics such as building team alignment, creating technical roadmaps under uncertainty, and navigating the challenges of the new leadership position.
Co-authors Dunya Kirkali and Maxim Schepelin draw from their experience leading engineering teams to focus on the unspoken rules of management. Their joint blog, "Incremental forgetting," emphasizes practical, data-informed leadership, a theme central to their new book. Schepelin, an engineering leader at Booking.com, often writes about deconstructing corporate jargon, such as rethinking "technical debt" as a "technical tax" to reflect its non-optional nature. The book's publisher, Leanpub, operates on a "Lean Publishing" model, allowing authors to release early and update their books based on reader feedback. This iterative approach mirrors agile development, a process Kirkali and Schepelin initially embraced after realizing their initial goal of a comprehensive handbook was too broad and that focusing on "tacit knowledge" was more valuable for new managers. For data engineers moving into management, understanding the modern data stack's evolution is crucial. The combination of Snowflake for scalable compute and storage, dbt for SQL-based transformations, and Airflow for orchestration has become a standard for building reliable data platforms. Snowflake's recent integration of its AI coding assistant, Cortex Code CLI, with dbt and Airflow aims to streamline data engineering workflows by reducing context switching for developers. At an enterprise scale, MLOps is shifting from a niche discipline to a strategic driver of business value, with a focus on reproducible, auditable pipelines and continuous monitoring of both data and model performance. For actuaries and underwriters, data quality is a primary concern, as inaccurate data can lead to mispriced policies and flawed risk models. The Actuarial Standards Board provides specific guidance on data quality, which is foundational for reliable actuarial analysis. Aspiring product managers will find that AI is significantly shaping consumer-facing industries. In fashion and retail, AI is used for everything from trend forecasting and virtual try-ons to personalizing customer experiences, which can increase order values and reduce returns. Product managers in this space leverage AI to analyze vast amounts of customer data, enabling more data-driven decisions and personalized product recommendations. The New York City tech scene is a hub for AI and data startups, with over 2,000 AI startups and 35 AI unicorns. Companies like Weights & Biases (ML tooling), Scale AI (data platform), and Chainalysis (blockchain data) are actively hiring for roles in engineering and product. The demand for AI and machine learning engineers, data scientists, and data engineers remains high, with salaries often exceeding national averages. In the realm of personal wellness, the focus is shifting towards science-backed, data-driven health. Wearable technology is the top fitness trend, with devices tracking metrics like heart rate variability (HRV) to offer a more complete picture of health and recovery. There's a growing emphasis on longevity, with a focus on personalized nutrition, sleep optimization, and sustainable muscle building over purely aesthetic goals.
