Everyone understands the phrase “looking for a unicorn.”
Are you that BI engineer with great people skills? “You’re a unicorn!”
Are you a data scientist with a talent for visualization? “You’re a unicorn!”
But everyone’s really looking for a platypus, that odd combination of duck, beaver, and otter. While looking for a new position last winter, I often received job descriptions (for which I was a perfect fit!) needing someone to build a database, gather and implement business requirements, design dashboards, and do predictive analytics to guide their business. These were all often lumped under the title “data analyst.”
It’s true that all business intelligence roles overlap. And the analytics field is evolving so rapidly that it’s no wonder recruiters and businesses struggle to sort out roles. Although the technical job market favors seekers right now, small to mid-size businesses still often ask for a Swiss army knife applicant who can do the ETL work of an engineer and create effective visualizations for leadership.
Even mature organizations sometimes don’t know what they’re looking for. A former colleague at a large software company in Redmond recently contacted me about an intriguing role (on a cloud product) for an international PM + data scientist. Nothing in my experience indicates data science, but I was very curious about what the team really needed. It turned out that “data scientist” meant someone who could write basic SQL queries (hooray – I am a data scientist!).
Another large tech company pulled me in for a loop where the previous contractor had been fired for failing to produce. I quickly realized why: data sources were largely unidentified, no one knew exactly where to find them, and the managers had conflicting views of the role’s core competencies. A call with a BI lead in another team confirmed that the hire would need to be both an engineer, capable of setting up cloud infrastructure to host large data sets, and a reporting analyst working with product managers to identify issues and new opportunities.
Putting on your business analyst hat while looking for a good fit is really helpful. The same questions that help tease out requirements (what are your end users really trying to do?) will define the role more clearly than a job description:
- What are you trying to accomplish, and what’s your top priority? (Is the emphasis on setting up the data, writing queries, building infrastructure, reporting insights, etc.?)
- Why do you need to do this? (Useful if the business goals aren’t clear.)
- What pieces of this (process or tools) are currently in place?
- If someone was previously in this role, what did they do well, and why did they leave?
- Who are my “customers” and key stakeholders?
- How would you prioritize the skills do you expect this person to have?
- Who else is on the team? (Will you be the only person in your role? What kind of support will be available? How mature is the company’s approach to analytics, and do they prioritize it?)
As the data analytics field matures, job functions will become better defined, just as they did in engineering, and it’ll become easier for seekers and recruiters to find each other. In the meantime, if you’re just entering the field and have the overarching role of gaining skills and just growing, embracing your inner platypus isn’t a bad way to go. Making the stretch from water to land, from fowl to mammal is best done in a swamp – which is to say, not in Washington D.C. but in a supportive, varied environment where management understands that you’re still growing a bill or learning how to swim. Analytics/BI is a young enough field that it’s still fairly easy to find colleagues whose varied backgrounds led them to analytics through a huge array of channels – finance, publishing, professional snowboarding, and many more. Most of us are crossing over from other fields and bring a wide array of experience with us. As a recruiter yelled when we found a good fit for me, “Viva la platypus, baby!”