The role of a data architect has fundamentally shifted in the past decade. Gone are the days of rigid, centralized systems and monolithic ETL pipelines. The modern data architect is no longer just a schema designer or database tuner—they are platform thinkers, enablers of decentralized data teams, and key players in building scalable, trusted analytics ecosystems.

In this post, I want to unpack what it means to be a modern data architect today, what tools you need to master, and how this role intersects with the rest of the modern data platform.

What You’ll Learn in This Blog Series

This blog series will evolve into a full course that covers:

Building a modern data platform from scratch (Snowflake + dbt + Airflow + Metabase)
Dimensional modeling, metrics layers, and semantic consistency
Setting up ELT pipelines with Airbyte and managing CDC with Snowflake Streams
Introduction and hands-on work with Apache Iceberg for lakehouse design
Data quality and testing using dbt and Great Expectations
CI/CD and GitHub Actions for dbt workflows
Implementing metadata lineage with OpenMetadata and OpenLineage
Designing secure, cost-optimized data platforms using best practices
Architecting for scale: federated platforms, Data Mesh, and more

Whether you're transitioning from analytics engineering or starting out, this series will help you think in systems—not just scripts.

The Shift from Legacy to Modern Data Architecture

Traditionally, data architects worked with on-premise databases, wrote intricate ETL pipelines, and operated as part of central IT teams. The modern landscape is completely different:

Cloud-native warehouses like Snowflake and BigQuery have abstracted away infrastructure.
dbt and ELT pipelines have empowered analysts to build models directly.
Lakehouse formats like Iceberg are changing how we store and access data at scale.
Metadata, governance, and observability are no longer afterthoughts—they’re essential from Day 1.

What used to be a backend role has become a strategic and cross-functional one.

What Does a Modern Data Architect Actually Do?

Think of the modern data architect as someone who connects the dots:

Designs cloud-native data architectures that scale with the business
Enables agile data modeling and transformation using tools like dbt
Makes data reliable through tests, CI/CD pipelines, and semantic layers
Establishes governance frameworks without blocking speed
Ensures the platform is observable, cost-efficient, and future-proof

If the analytics engineer is building the road, the data architect is designing the city. It's about setting up systems that scale—technically and organizationally.

Key Responsibilities

Here’s a breakdown of what the role usually includes:

Architecture Design: Choose between centralized vs federated, batch vs streaming, lakehouse vs warehouse.
Pipeline Enablement: Empower teams with ELT pipelines using dbt, Airbyte, or Fivetran.
Data Modeling: Drive standardization across domains while allowing flexibility.
Security & Access Control: Implement RBAC models in Snowflake or similar platforms.
Data Quality & Testing: Ensure contracts and tests exist across the pipeline (dbt + Great Expectations).
Lineage & Metadata: Integrate OpenMetadata, OpenLineage for visibility.
Cost & Performance Optimization: Tune Snowflake usage, implement incremental models.

Core Skills You’ll Need

The stack has changed. So have the expectations. Here's what you need to know:

SQL: Still king. Especially analytical SQL for transformation.
dbt: The core of the modern modeling stack.
Git: Version control and collaboration.
Cloud Data Warehouses: Snowflake, BigQuery, Redshift, Databricks.
Orchestration: Apache Airflow, or tools like Dagster.
Lineage & Metadata: OpenMetadata, dbt artifacts, event logs.
Table Formats: Iceberg (my favorite), Delta, Hudi—especially for lakehouse designs.
Security & Governance: RBAC, data contracts, audits.

It’s not about knowing every tool, but understanding the moving parts and how they work together.

The Stack of a Modern Data Platform (2025 View)

Here’s what a standard setup might include:

Ingestion: Airbyte or Fivetran
Storage: Snowflake or Iceberg table formats on object storage
Transformation: dbt
Orchestration: Airflow
Testing & Contracts: dbt tests, Great Expectations
Lineage & Cataloging: OpenMetadata
BI/Exploration: Metabase, Hex, or Looker

This isn't theory—this is what I build with and what I’ll cover throughout this blog series.

Final Thoughts

Being a modern data architect isn’t about buzzwords. It’s about making data trustworthy, accessible, and scalable. You don’t just build pipelines—you build systems that help entire teams succeed.

In the next post, I’ll walk through setting up your first dbt project and explain how it fits into this bigger picture.

Let’s build the modern data platform—one blog at a time.

What Is a Modern Data Architect? Skills, Tools, and Responsibilities