Learning to become an analytics engineer is new and messy. No one graduated with a Masters in Analytics Engineering (as far as we know!). The vast majority of us learned on the job and most of us worked an “Analytics Engineering type role” before it was even called that.
Emily Hawkins and I teamed up in November of last year to teach Analytics Engineering with dbt on Uplimit (formerly CoRise) to help more people learn these powerful skills, whether they are already an Analytics Engineer wanting to learn best practices, or they want to transition into this type of role, or if they are just curious what all the buzz is about.
We have finished 3 amazing runs of the course so far with learners successfully completing from 250+ companies with a range of roles coming in.
We’ve even had several learners already make the leap into Analytics Engineering careers! Given huge demand, we’ve even partnered with Uplimit to make an entire Analytics Engineering Track learners can gain fluency with the essential building blocks of SQL, Python, and dbt.
From the beginning, we felt training needs to mimic on-the-job realities with courses taught by industry practitioners. Needy stakeholders, messy data, stale data, vague questions that need clarification, open ended challenges that have multiple potential approaches…that’s the job and we packed all of it in our month-long course! Throughout the 4 weeks, we explore the modern analytics stack and best practices to transform, test, and maintain datasets within a cloud data warehouse using dbt.
We assign each student the role of the first analytics engineer at our company “Greenery” (we sell house plants, get your head out of the gutter!), in charge of setting up our dbt project, modeling data, and helping our company make smart data-backed decisions.
For this run, we are super excited to make this even closer to on-the-job realities with two new popular industry tools integrated into the class project experience. We think this will make it our best run yet!
First, in addition to dbt, we will use modern tools like Snowflake as the data warehouse. Snowflake has become the dominant cloud data warehouse – Emily and I both use it in our job daily! We know students want to run back to their desks (or switch tabs in the modern age) and apply their work at their companies, many of whom use Snowflake, and this makes the work even more realistic.
Secondly, we are using Sigma for data visualization. Sigma is a BI analytics tool purpose-built for your cloud data warehouse, including Snowflake. One of the most crucial skills to have working in data, is the ability to partner with stakeholders to provide information in a meaningful way, and so in the second part of the course, we ask learners to analyze our product funnel – How are our users moving through the product funnel? Which steps in the funnel have largest drop off points? – to make data informed product decisions. Maybe our checkout step is causing too much friction, or maybe no one gets to product pages at all to check out our beautiful plants! We’ll use Sigma to easily visualize the funnel, and to support other analysis in the course.
We are also extremely excited that Sigma is offering 5 scholarships for this run particularly focused on learners disproportionately underrepresented in the data and technology sector. If you are interested in a FULL scholarship – $500 value – please apply here.
♥️
Emily and Jake