Key Skills and Tools for Analytics Engineers: Building the Future of Data

Welcome back to my Analytics Engineering series! So far, we’ve looked at what analytics engineering is and why this role is taking centre stage in the modern data stack. Now, we’re talking about the skills and tools every analytics engineer (AE) should have in their skillset.

Whether you’re a Data Professional looking to break into the field or a company building a best-in-class data team, understanding these skills and tools is important!

Must-Have Skills for Analytics Engineers

Analytics engineers are a unique blend of engineer, analyst, and storyteller. Their skill set shows this too, with a blend of tools like:

Strong SQL Skills

• Vital for handling and transforming raw data, creating solid data models that reflect business needs, and making sure queries are efficient and easy to understand for collaborative work.

Data Transformation Expertise

• Involves cleaning up and reshaping raw data into usable models, designing different stages of data tables (like staging, intermediate, and final), coding business rules into reusable structures, and rigorously checking data accuracy.

Familiarity with the Modern Data Stack

• Need to be comfortable with tools like cloud data warehouses (e.g., Snowflake, BigQuery, Redshift), ETL/ELT tools for moving data around, and transformation tools like DBT for smarter data modelling.

Software Engineering Principles

• Using things like version control (like Git) to track changes in data models, automated testing to make sure transformations are spot on, and checking code to keep everything top-notch.

Communication and Business Acumen

• Being able to turn business questions into tech tasks, working closely with others to meet their data needs, and explaining findings in a way that shows how they make a difference to the business.

The Tools Shaping Analytics Engineering

Now that we’ve covered the key skills, let’s spotlight the tools every AE should know. These tools are the backbone of the modern data stack.

DBT (Data Build Tool)

• DBT revolutionizes analytics engineering by enabling direct data transformations in the warehouse using SQL. It supports version control for transformations, ensures data accuracy through tests, and enhances collaboration with documented models. It brings crucial software engineering practices to analytics workflows.

Cloud Data Warehouses

• Cloud data warehouses like Snowflake, BigQuery, and Redshift are central hubs for storing and transforming raw data. Each platform, from Snowflake’s scalability to Big Query’s serverless capability and Redshift’s Amazon integration, offers unique strengths essential for any AE to master.

BI Tools

• While AEs are not always responsible for building dashboards, familiarity with BI tools like Looker, Tableau, or Power BI is advantageous for AEs. It helps them understand how data models are utilized and interpreted by business users.

How These Skills and Tools Benefit Teams

For Data Professionals:
Mastering these skills and tools opens doors to exciting opportunities. Analytics engineering is one of the most in-demand, high-impact roles in data today.

For Employers:
A well-equipped AE ensures your data infrastructure is solid and scalable. They’ll help you move faster, reduce errors, and unlock the full potential of your data.

Final Thoughts

Analytics engineering is all about combining technical expertise with business insight. By mastering key skills like SQL and data modelling and using tools like DBT and Snowflake, analytics engineers can transform raw data into a strategic asset.

In the next article, I’ll discuss the future of analytics engineering and what’s on the horizon for this exciting field. Whether you’re a data professional or employer,  the world of analytics engineering offers endless possibilities. I’m  happy to help with any growth needed in your team!

View the previous article in this series:

Part one: Analytics Engineering: Modern Data Teams
Part two: The Rise of Analytics Engineers: The Modern Data Stack