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Sydney Bang

Data Analytics Engineer at Spotify

How I became an Analytics Engineer at Spotify 2 years after graduating with an Exercise Science Degree

December 9th, 2022
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Hi friends, I’m Syd! I am Spotify’s first ever associate analytics engineer. Nov 2023 marks 2 years since the beginning of my shift from working in the Customer Success/Operations space at my first full-time role after graduating from Kinesiology at a small Toronto-based startup to now working on the first analytics engineering team as an associate AE within Spotify’s music content analytics team.

Over the past few months I’ve had a handful of coffee chats, sat on some career/student/alumni panels and tons of back and forth on LinkedIn, etc., so I’ve decided to compile a few of the most common questions into a short read.

The top 3 most common questions I’ve gotten are:

1. What the heck does an analytics engineer do? What does your day-to-day consist of?

2. What was the interview process like?

3. How did you transition from graduating in Kinesiology to working as an analytics engineer and why? Can you describe your career journey?

What the heck does an Analytics Engineer do? What does your day-to-day consist of?

TBH I was going to try to write a little blurb butMitchell Silverman
has done a way better job here. He’s currently managing our team of 4 as the first analytics manager in the organization and we both work on the music side of content analytics at the company. This means we get to work with data scientists and data engineers to build, test, deploy, and maintain data pipelines. We support the parts of the platform that touch playlists, charts and records, streams, license & label partnerships, and a bunch of other really cool things! As an associate analytics engineer, I get a ton of support from my manager as well as my coworkers that work on other parts of the platform (podcasts, audiobooks, and a handful of other audio initiatives). Most of my days recently have been spent migrating our legacy internal data processing tool into our shiny new dbt project. If you have more questions about the specifics, feel free to reach out to me on LinkedIn and I can do my best.

What was the interview process like?

The timeline of my interview process from seeing the open role to getting my official offer took exactly 1.5 months (43 days).

When I initially saw the posting for the role, they were looking for a mid-ish level analytics engineer with about 5+ years of experience. I had 0 years of experience as an analytics engineer and just under 11 months of experience as an associate data analyst at the time but I applied anyways (alongside a referral I asked a machine learning engineer I overlapped with for most of my time at Kickstarter) because the team was doing exactly what I was looking for in my next role: bringing dbt to, and building out the project within an organization. This is might sound like a very particular or specific thing to be looking for and if you’re wondering how exactly I came to this decision, keep reading!

There were around 6–7 rounds with the recruiter, the hiring manager, some members and stakeholders on the team as well as 2 technical screenings and a take-home assignment. During the first technical screening, the team recognized that I definitely did not have 5+ years of experience, but agreed to move forward with me as an associate AE because they valued the potential for growth and wanted to set the standards as the first analytics engineering team at the company.

I might go into more detail about the interviews in another piece but the only question I remember off the top of my head was my (now) manager asking what I like to do for fun lol. A few rounds later and after some negotiation, I was starting as the company’s first ever Associate Analytics Engineer. How exciting!

How did you transition from graduating in Kinesiology to working as an analytics engineer? Can you describe your career journey?

Throughout the duration of my degree, I knew that I probably wasn’t going to pursue a career in Kinesiology afterwards. I spent a lot of time in the athletic therapy clinic as a student and an athlete receiving treatment, as well as working for clinical hours at my chiropractor’s office. Through hours spent in the clinic and conversations with the people I was shadowing, I felt drained by the lack of growth opportunities and the fitness industry as a whole felt too gimmicky and washed out for me. I ended up completing the degree instead of trying to change majors or dropping out just because I genuinely enjoyed the courses I took and the friends I took them with, and liked learning in general—just not enough to pursue a career in this field.

In my last semester of university, I started exploring the tech industry because I valued the idea of remote work and the ever-changing landscape that allows you to always be learning. I looked around for roles ranging from operations, customer success, product/project management, and software development/engineering. My first job out of post-secondary was a customer success role so the only “data work” I really came across in my day-to-day was migrating legacy client data into our platform (it was more of a direct copy and paste, click here, do that, don’t click this, kind of migration though…) I didn’t understand anything I was doing and I didn’t even realize until a few months into my first data role that I was actually copy and pasting SQL that one of our engineers had written the whole time.

I poked and prodded around to see if I would enjoy pursuing a career in Customer Success and after a year or so and a handful of pretty great industry leading mentors through‘s coaching corner (a community built for growing careers in Customer Success), I realized that I actually absolutely hated Customer Success. Although I now recognize that Customer Success wasn’t for me, I look back at my time in CS fondly and am grateful for the mentors and co-workers I’ve met along the way that have helped me discover how I truly enjoy contributing most.

After I was fully onboarded into that role, I started digging around asking the engineers what they do, how they learnt what they know now and askeD if they can pair on some of the more technical work they do so I can catch a glimpse of their day-to-day. There’s something about the collaborative, life-long learner-esque, open-source nature of engineering that really drew me in. I love working with others and although engineering doesn’t sound like a team sport, engineers are some of the most willing and helpful people I’ve ever met. So with that in mind, I decided that I wanted to learn how to code and eventually become an engineer of some sort.

One problem: I barely knew the difference between a front-end and a back-end developer. What’s a software engineer? What the heck is an IDE? I had no idea where to begin or what to learn.

My options as an exercise science graduate were:

  1. Do another undergrad? Hell no, I don’t want to spend another $40k and 4 years in school.
  2. Sign up for a coding bootcamp? 3–6 months full-time and $10–30k sounds like more commitment than I’m ready for because I didn’t know if I was “smart” enough
  3. Try the self-teaching route? How would I structure my learning, who would I go to for help if I’m stuck and how much time will I actually commit to this?

I opted for BrainStation’s 5 week part-time Introduction to Data Analytics course (I think it was about ~$3500CAD but I only paid around $2000CAD because my company at the time had a $1500 education stipend) because I already spent a few months while I was in school trying to go through ‘s Responsive Web Design certification (which I don’t think I ever completed in full), General Assembly’s interactive HTML, CSS and Javascript course (I think I finished this one), and sifting through ‘s HTML & CSS and Python catalog.

I don’t remember exactly what drove my decision to choose data analytics/science but it probably has something to do with the 80 million Medium articles about how sexy Data Science is, and the massive data science and tech community on Youtube and other platforms— with the hopes of “levelling up” to a data scientist.

The Data Analyst to Data Scientist pipeline

Once I finished my part-time 5 week course I kind of fell in love with writing SQL. I spent my mornings before work and a few hours after work studying, practicing my SQL and trying to get a grasp on other data-y things. I applied for a few data analyst roles just to test the waters and see what companies are looking for so I can hone in on my studies and focus on “hire-able skills”. The first company I heard back from was Kickstarter, an NYC-based crowdfunding company (PBC).

I moved along the 5 or 6 step interview process until I somehow stuck the landing—a few weeks later I was starting in my first associate DA role. What a dream! Over the year I was at Kickstarter, I was able to gain a solid footing in SQL—the staple programming language for anyone who works in data; Looker, the BI tool of my company’s choice (although I learnt Tableau in the intro course I took); as well as get exposed to dbt, the data transformation tool that is the hot topic of debate in the conversation around data engineers, data scientists and data analysts.

Big shoutout to dbt and their community— They do an incredible job of maintaining one of the most inclusive and educational communities I know of. dbt has defined the role of the analytics engineer, a “newer” career that has really evolved over the past few years. This short article (and this one too actually) does a great job of explaining how my mindset shifted from wanting to pursue analytics engineering over data science/machine learning. dbt has played a huge, if not the biggest role in this story for me as this is how I met my current mentor!

The juice

A year into my role at Kickstarter, I asked my then-manager about transitioning my role to an analytics engineer. I was told that it would take some time to define Kickstarter’s first AE role and that there was still a lot of learning I had to do in my current role so this would probably be a conversation for next year’s promotion cycle. I had some chats with my mentor, Erica (big shoutout xoxo love u ❤), about how I can continue up-skill in the meantime and she encouraged me to take one of two courses: Analytics Engineers Club’s From Analyst to Engineer in just 10 weeks or Uplimit’s Analytics Engineering with dbt (5 weeks).

I went with the Uplimit route (about half the price and the time at $500USD for 5 weeks instead of ~$1000USD and 10 weeks) and highly recommend others to take a look at their course catalogue for tons of courses from SQL to ML/NLP courses. With the support of not only the great course instructors and TA’s, but the rest of the Uplimit staff (shoutout to y’all), they facilitate an interactive form of online education (different from Coursera, Udemy, etc) that includes a blend of scheduled + async work leaving you with a piece of work you can add to your portfolio. Even before this course began, I decided to start looking out for AE roles I could begin interviewing for to—you guessed it—test the waters and see what companies are looking for so I can hone in on my studies and focus on “hire-able skills.”

At this point in my career, I didn’t think it was possible to land my second data role ever at a company like Spotify, but I knew that I could learn a lot from the interview process to help prepare myself as I looked for analytics engineering roles in the future. One month later, official offer was sent, signed, and received! Pretty crazy, right mom?

Teams and companies are always evolving, especially in the data space.

When Spotify was looking to hire, they were looking for someone to hop right in and help grow their dbt ecosystem but when I interviewed, they realized that as the first and currently only (at the time of writing) analytics engineering team, they want to focus on growing the analytics engineering ecosystem within the organization and building out a more robust career path for analytics engineers as whole. Now instead of having a handful of mid-level engineers, we get to have a team with different skill sets and levels of experience!

Everyone has a different path to how they got/get to where they are. As an early-career data practitioner (hehe), it was reading articles like this over the past year or two that helped me understand what parts of the data world I was curious about, what areas of opportunities I was interested in pursuing, and gave me the confidence to keep learning and take chances on the opportunities presented to me. Every single person I’ve met along the way thus far from mentor, manager, co-worker, stranger in the data Twitter space (rip lol), etc; thank you for being part of my journey. I hope I can play any part of a positive role in other people’s journeys as you all have in mine!

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