Engineering Craft
The proactive breadth layer for ML, SDE, and AI engineers: product sense, DevOps and platform fundamentals, data engineering patterns, and staff-level technical leadership. Not debugging production — designing better systems, making better decisions, and growing your scope of impact.
guides
Backend Engineer Interview Prep Path
A 9-week structured roadmap for Backend Engineer interview preparation — DSA to LLD to HLD to PROD-ENG. Covers how each area is weighted at IC3 through IC6, why grinding 500 LeetCode problems beats understanding 50 patterns, and the operational maturity signals that separate L5 candidates from L6 at Google, Meta, Amazon, and Stripe.
Data Scientist Interview Prep Path
An 8-week structured roadmap for Data Scientist interview preparation — SQL to Stats to Analytics to ML to Scenario questions. Covers what FAANG DS loops actually test, why most candidates fail on window functions and A/B testing math rather than ML theory, and how to identify whether you should apply for DS vs MLE vs Analytics Engineer roles.
CI/CD Pipelines: Designing Safe, Fast Delivery for ML and SDE Systems
How to design a CI/CD pipeline from scratch — test pyramid structure, artifact promotion strategies, deployment patterns (blue-green, canary, feature flags), and the specific tradeoffs that apply to ML model serving. Tested in platform engineering, SRE, and senior SDE interviews. Distinct from debugging a broken pipeline in production — this guide covers proactive pipeline architecture.
Data Engineering Pipelines: Reliability, Quality, and Evolution
How strong engineers design data pipelines that are observable, backfillable, and resilient to schema drift, late data, and silent data quality failures. Covers batch vs. streaming tradeoffs, Lambda vs. Kappa architecture, exactly-once semantics, Airflow DAG design, Flink checkpointing, and the monitoring patterns used by production data teams at Airbnb, LinkedIn, and Netflix.
How to Approach Craft Interviews: Behavioral, Incident, and Technical Communication
Craft interviews test your engineering judgment, communication, and leadership through behavioral questions, postmortem walkthroughs, ship-or-not decisions, and technical writing scenarios. This guide gives you the IMPACT framework for behavioral answers and the RESPOND model for incident questions — the systematic approaches that separate Staff engineers from Senior engineers in L5/L6/E6 interviews at Meta, Google, Amazon, and Stripe.
STAR Behavioral Interview Stories: Structure, Archetypes, and Leveling Signals
Master STAR behavioral stories for FAANG: Amazon Leadership Principles scoring, Google's Googleyness rubric, and Meta's impact-at-scale bar. Five fully-worked story archetypes with quantified results, plus leveling signals that separate L4, L5, and L6 answers.
How to Be a 10X Engineer: Leverage, Reliability, and Team Multiplication
A practical, non-hype framework for becoming a high-leverage engineer. Learn how top engineers multiply team output through system design, execution reliability, prioritization, and mentorship instead of heroics or long hours.
Data Modeling for Product and Analytics Systems
A practical guide to data modeling decisions that improve product velocity and analytical trust. Covers entity design, normalization tradeoffs, event modeling, and schema governance.
Leadership Influence for Engineers: Driving Outcomes Without Authority
How senior and staff engineers influence product direction, platform decisions, and cross-functional outcomes without formal management authority. Covers stakeholder mapping, narrative framing, options memos, pre-wiring alignment, and decision artifact practices — the exact behaviors that separate engineers who are 'technically excellent' from those who move organizations.
Mentoring and Growth in Engineering Teams
A practical framework for mentoring engineers effectively across levels. Covers growth diagnosis, feedback loops, stretch assignment design, dependency avoidance, and how to create compounding team capability instead of one-off coaching. Essential for engineers moving into senior and staff influence roles where multiplying others is a core expectation.
Product Metrics & North Star: How Engineers Define and Own Success
How to design a measurement system before you write code — selecting a north star metric, decomposing it into actionable driver trees, and choosing guardrail metrics that prevent gaming. Tested explicitly at senior and staff levels at Meta, Google, and Airbnb, and the skill that separates engineers who build impactful features from those who build busy ones.
Engineering Strategy: Turning Technical Direction into Business Outcomes
A practical framework for engineering strategy: selecting focus areas, sequencing investments, and balancing reliability, velocity, and platform leverage over multiple quarters.
Staff+ Engineering Interviews: Strategy, Ambiguity, and Org-Level Technical Leadership
A practical playbook for Staff+ interview loops focused on technical strategy and organizational impact. Covers ambiguity framing, roadmap shaping, design-doc influence, disagree-and-commit, and risk-managed execution at L6+ scope.