Hi 👋 from dbt Engineer

Discover how styling transforms your dbt workflow—from smart naming and clean SQL formatting to dynamic Jinja, YAML clarity, and enforced standards with SQLFluff and dbt‑checkpoint. This guide turns pipelines into readable, maintainable stories that boost onboarding and trust.

Discover dbt 1.10: lightning‑fast iteration with the new --sample flag, smarter batch hooks, snapshot hard‑delete tracking, SQL‑based freshness, Python 3.13 support, catalog parsing, and enhanced YAML/JSON validation—plus behavior flags to upgrade safely.

Learn how to seamlessly run your dbt Core project with Apache Airflow. We cover the different methods to setup, code repo, video tutorial for local dev, and best practices to help you orchestrate dbt runs using Airflow: the easiest way to modernize your data workflows.

Discover how to supercharge your dbt models using Jinja loops! This guide explores practical loop patterns for clean and DRY SQL transformations.

Struggling with "maximum recursion depth exceeded" in dbt macros? This article explains why it happens—usually due to missing or misused variables—and shows simple, beginner-friendly steps to fix it, ensuring your dbt projects run smoothly and error-free.

Learn how to standardise logging in dbt macros for clear, consistent CLI output. This guide shows you how to create a reusable logging macro, making debugging and tracking macro activity in your dbt projects easier and more efficient.

Automate PII tagging in your dbt project using OpenAI! Streamline data governance, ensure compliance, and save time by leveraging AI to detect and flag sensitive information in your data models. Integrate this workflow into CI/CD for robust, scalable data protection.

If people don’t trust the work of the analytics engineering team, then all the effort you put in doesn’t matter. One of the best ways is by using test-driven development (TDD).

Unlock the power of Analytics Engineering to transform your data into actionable insights.

Explore the pros and cons of dbt monorepo vs multi-repo setups. Learn when to use each for scalable, collaborative analytics engineering workflows.

Follow a real-world journey from ETL to ELT. Learn how a modern data team overcame bottlenecks and scaled fast using cloud warehouses and dbt.

Analytics Engineers bridge the gap between raw data and business insight. This guide breaks down what they do, the tools they use (like dbt and SQL), and why they're essential to modern data teams. If you're building trusted data models, this is where you start.

Discover how styling transforms your dbt workflow—from smart naming and clean SQL formatting to dynamic Jinja, YAML clarity, and enforced standards with SQLFluff and dbt‑checkpoint. This guide turns pipelines into readable, maintainable stories that boost onboarding and trust.

Discover dbt 1.10: lightning‑fast iteration with the new --sample flag, smarter batch hooks, snapshot hard‑delete tracking, SQL‑based freshness, Python 3.13 support, catalog parsing, and enhanced YAML/JSON validation—plus behavior flags to upgrade safely.

Learn how to seamlessly run your dbt Core project with Apache Airflow. We cover the different methods to setup, code repo, video tutorial for local dev, and best practices to help you orchestrate dbt runs using Airflow: the easiest way to modernize your data workflows.

Discover how to supercharge your dbt models using Jinja loops! This guide explores practical loop patterns for clean and DRY SQL transformations.

Struggling with "maximum recursion depth exceeded" in dbt macros? This article explains why it happens—usually due to missing or misused variables—and shows simple, beginner-friendly steps to fix it, ensuring your dbt projects run smoothly and error-free.

Learn how to standardise logging in dbt macros for clear, consistent CLI output. This guide shows you how to create a reusable logging macro, making debugging and tracking macro activity in your dbt projects easier and more efficient.

Automate PII tagging in your dbt project using OpenAI! Streamline data governance, ensure compliance, and save time by leveraging AI to detect and flag sensitive information in your data models. Integrate this workflow into CI/CD for robust, scalable data protection.

If people don’t trust the work of the analytics engineering team, then all the effort you put in doesn’t matter. One of the best ways is by using test-driven development (TDD).

Unlock the power of Analytics Engineering to transform your data into actionable insights.

Explore the pros and cons of dbt monorepo vs multi-repo setups. Learn when to use each for scalable, collaborative analytics engineering workflows.

Follow a real-world journey from ETL to ELT. Learn how a modern data team overcame bottlenecks and scaled fast using cloud warehouses and dbt.

Analytics Engineers bridge the gap between raw data and business insight. This guide breaks down what they do, the tools they use (like dbt and SQL), and why they're essential to modern data teams. If you're building trusted data models, this is where you start.