Research
I love to teach. I post about #dataengineering and #mentalhealth daily.
Many years of data engineering experience doing growth analytics at Facebook, security infrastructure at Netflix, and pricing and availability infrastructure at Airbnb.
You can find me by looking up EcZachly on all social networks
Join my Data Learning Platform https://www.dataexpert.io
Adam Markovitz's published DoMLs
The workflow analyzes weekly sales data to surface meaningful trends, anomalies, and performance insights for a sales analytics team. It identifies significant week-over-week and quarter-over-quarter changes, highlights rep-level outliers, and produces concise, actionable recommendations. The final output includes a short executive summary suitable for leadership review.
The goal is to help a customer success team proactively identify accounts that are at risk of churn. By analyzing activity logs, health scores, and renewal timelines, the system classifies churn risk levels and recommends appropriate interventions. This enables customer success managers to focus on high-value and high-risk accounts with timely, relevant outreach.
The workflow helps product teams interpret A/B test results by assessing both statistical and practical significance. It standardizes decisions on whether to ship variant B, stay with A, continue testing, or redesign the experiment. The workflow ensures rigorous, consistent evaluation using predefined confidence and lift thresholds.
The goal is to segment users into a small number of actionable groups based on behavioral patterns to support targeted marketing campaigns. The workflow must analyze event behavior, user attributes, and account age to generate clear segment labels, defining characteristics, messaging themes, and estimated segment sizes. The segmentation must emphasize behavioral signals and avoid creating overly small clusters.
Zach shared insights on completing a data analytics bootcamp and reinforcing skills in Airflow, dbt, Snowflake, and data engineering.
View original on LinkedIn →Zach shared insights on AI’s evolving impact on data engineering and the shift toward intent-driven, automated systems.
View original on LinkedIn →Zach Wilson published an article about building an end-to-end flight delay prediction pipeline using Snowflake, dbt, and machine learning.
View original on LinkedIn →Zach Wilson discussed building and debugging an end-to-end RAG pipeline with ingestion, embeddings, and vector search.
View original on LinkedIn →Shared content about analytics-focused capstone work on creator metrics and behavior in a technical bootcamp
View original on LinkedIn →