The full context of analytics engineering is to be found in The Analytics Engineering Guide.
The Analyst, The Engineer, The Visualizer
Since the problem space is so varied, we align our analytics professionals with the listed business area verticals rather than organizing them within a single functional horizontal. The expectation is that individuals in these roles possess deep business context and are thought leaders alongside their business counterparts. This enables them to fully understand where their partners are coming from. It also means Analytics and Visualization Engineers are a specialized resource and a rare commodity. There are many more questions and stakeholders than analytics team members, and the job is not to take on every request. Instead, these individual contributors are given freedom to choose their projects and are responsible for prioritizing the ones that will have the most business impact (and deprioritizing the rest). This requires a lot of judgment and embodies our "context not control" culture.
Analytics engineers deliver well-defined, transformed, tested, documented, and code-reviewed data sets. Because of the high quality of this data and the associated documentation, business users are able to use BI tools to do their own analysis while getting reliable, consistent answers.
Analytics engineers are like librarians. They curate the catalog so that the researchers can do their work more effectively.
- Is it possible to build a single table that allows us to answer this entire set of business questions?
- What is clearest possible naming convention for tables in our warehouse?
- What if I could be notified of a problem in the data before a business user finds a broken chart in Looker?
- What do analysts or other business users need to understand about this table to be able to quickly use it?
- How can I improve the quality of my data as its produced, rather than cleaning it downstream?
As data tools changed, so did the people who used them. People who weren't on data teams began developing data literacy. This was good: business users wanted to self-serve and be data-driven. The downside was that these people often knew just enough SQL to be dangerous. If you've ever been to a meeting where two executives have different numbers for the same metric, you've experienced the result of this.
He wasn't interested in laying the foundation for an analytics function that scaled with more people, he wanted to build an analytics function that scaled with code.
So instead of churning out monthly Excel reports, Sagar's job is now to:
- Extract data from front-end tools and load it into Snowflake using an automated data pipeline tool. He chose Stitch.
- Transform data using dbt to write SQL transformations within the data warehouse.
- Deliver clean, tested, and ready-to-use data to Looker for analysis.
Analyze data and provide insights and recommendations across the organization.
- Manage requests
- Maintain a tidy dbt project
- Test data
- Educate others
They have more engineering skills than your average analyst and are more curious to solve analytical business questions than your average data engineer.
An analytics engineer is a technical analyst that applies software engineering best practices to the production and maintenance of analytics code. The analytics engineering workflow cleans and transforms raw data into consumable information and business logic. In the process, the analytics engineering workflow tests data to ensure it is of high quality, documents all business logic, and ensures data models are running reliably in a production environment.
For modern data teams, the ideal setup is for analysts, who have a much better understanding of the business logic that goes into data transformation, to own most or all of the transformation process. (ETL vs ELT)
They'll likely write better SQL than either your data analysts or your data engineers.
SQL is the key here as the whole analytics engineering is built upon it.
Data is less trustworthy than ever and people are fired up about it. Often times, an analytics engineer is really just a pissed off analyst who has the tools and motivation to make things better for everyone else.
- The explosion of data and SaaS tools combined with the ease of automating “Extract and Load” means you can have true unadulterated data chaos in under an hour.
- Analysts can no longer sit idly by and fix their data in the visualization layer or wait on tech teams to build a better pipeline. The analytics engineering movement will take back control over data quality and insights.
A few key trends are converging:
- The shift from ETL to ELT means folks who know SQL have the power to transform and clean data once it arrives in the warehouse.
- Data warehouses are powerful enough to handle the transformation workload
- Tools like dbt enable analysts with SQL-based workflows and enable them to work like software engineers do.
- Data consumers have come to simply expect data at their fingertips yet at all times.
- Organizations (large and small) are recognizing the need to invest more resources in data modeling.
- The work of an Analytics Engineer seems easy to understand, almost banal. They combine data sources, apply logic, make sure there are clean and well modeled materializations to analyze.
- As analytics engineers increase the amount of insight organizations can find from data, it actually becomes more likely that these orgs will want to hire additional data talent (both analytics engineers and analysts). Also, every organization needs to learn from their data and since the ways in which the data needs to be understood will be unique at every organization, they will all need analytics engineers.
- If it feels like we’re at a real inflection point for Analytics Engineering — it’s because we are. The impact is just too high.
- City governments will use analytics engineering to monitor programs and ensure that government resources are being used effectively. Academic institutions will use analytics engineering to create datasets, many of them public, that will aid in scientific and technological development. The possibility space is wide open.
- Analytics engineering is fundamentally a discipline that’s about making sense of the world around us. It’s about allowing everyone in an organization to see a little bit further, in their impact on the org and how their work connects to it. Right now analytics engineering is still a new discipline — pretty soon it will be everywhere.
Their job is to:
- Write production-quality ELT code with an eye towards performance and maintainability
- Coach analysts and data scientists on software engineering best practices (e.g., building testing suites and CI pipelines)
- Build software tools that help data scientists and analysts work more efficiently (e.g., writing an internal R or Python tooling package for analysts to use)
- Collaborate with data engineers on infrastructure projects (where they advocate for and emphasize the business value of applications)