Tips for a High-impact Career in Data + Series Summary
A guest post by a successful data professional who has been through multiple hype-cycles in data + Burning Questions, Answered series summary
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Deb is a close collaborator and I’ve learned a lot about data engineering just by listening to him talk during our jam sessions. He is one of the few people I know who is not only deeply knowledgable but is also equally passionate about helping others learn about data products. He actively mentors current and aspiring Data PMs and also writes an occasional newsletter on the topic.
Deb is currently the VP of products at InfinyOn, a composable event streaming platform built from the ground up. Prior to that, he has been a Data PM at multiple startups.
Old readers know that I publish guest posts rather infrequently – only when I know the content is useful and actionable. And because many of you are aspiring data professionals, you should certainly find Deb’s ideas and tips useful.
Unrelated but I recently concluded the 5-part series titled Burning Questions, Answered and you find the link to the series at the bottom of this post, followed by the contest details.
Over to you Deb! 🥁
Cracking the Code to a High-impact Career in Data
How do you navigate a career in data with all the hype and the information overload? How do you identify the breadth and depth of skills to acquire and master? How much time to invest in learning? How do you grow in your current role? How do you prevent yourself from going around in circles like a dog chasing its tail?
Well, I went from being a siloed data and software engineer to leading technical product teams. I could not believe how simple it was as a mental model. By simple I don’t mean easy; I mean clear and achievable. All it took was focused effort and direction.
Navigating professional growth has its complexities. Market conditions are always in flux. It is even more complex in technology, data, and growth.
New ideas come up at an unprecedented pace and scale. Tools and technology vendors, consultants, and competition among the bigger players fuel these ideas to products and features that ride the hype curve – it’s a never-ending cycle.
By the end of this short article, you’ll have a solid mental model to approach this problem and adapt the solution to your unique situation. This is the post I needed as I navigated my data career in 2010 and beyond.
Data career expectations
You will encounter tremendous hype on data roles, including high pay, and skill shortages. This is true in other areas too but our focus is on data. Database administration, data engineering, data analysis, data science, and similar labels are touted as a dream career.
The first thing to do is to get grounded and specific on a basic set of things that you would like from your career and things that you would NOT like. In my experience, it is easier to start with things that you don’t wish for, than to be specific about what you want. Try to start with a list of options based on current skill levels and things that you are curious about.
Trust but verify
Whether it’s roles, skills, or technologies, there will always be noise about new and popular ideas disguised as game changers and silver bullets.
By default don’t take anyone’s word for anything, and validate claims empirically before buying into the ideas. Always begin by evaluating who is presenting the idea and what is their incentive. Embrace a healthy skepticism and spend a bit of time thinking about the counterarguments to popular ideas online.
For example, you can compare and contrast software engineering, data engineering, data analysis, and data science. What are the common patterns in these roles, and what are the differences in the expected outcomes, baseline day-to-day responsibilities, challenges, and risks? Talk to some folks in the roles to get insights from someone who is further along in their career.
Apply the same parameters to this article. Now that we have the basics let me share the mindset power-up.
The code to strategic thinking
A severe gap in most data careers is strategic thinking. It’s not that data folks are not strategic, but there is too much work to do. Too many ad-hoc requests. Too many fires. And insufficient business context.
But strategic thinking is expected of folks working with data by default.
And it’s an incredible power-up to a data professional’s career to talk through different scenarios on the board, the different positions and moves, and the possible results.
Trends and hypes are all about change. But long-term strategy needs farsight – it’s based on things that don’t change as often.
There are two tools that you can use to build your strategic muscle: working backward and zooming in and out.
Alright, I’m back! Click the button above to read the rest of Deb’s post.
Burning Questions, Answered: Series Summary
Burning questions is a term I use for data questions whose answers aren’t just nice-to-haves or meant to serve one’s curiosity; instead, the person asking knows exactly what to do with the answer.
The two distinct characteristics of a burning question are:
It is very specific
It has context baked in
In the series, Burning Questions, Answered, I start by introducing the idea by referencing the burning questions I had when I led growth at Integromat (Make), followed by a deep dive into the process of answering burning questions.
Here’s what the series covers:
Part 3: Where Does the Data Originate? Internal Sources Explained
Part 4: Where Does the Data Originate? External Sources Explained
Jump directly to the individual parts of check out the entire series here.
And remember, if you have questions, I’d love to answer them so feel free to leave them in the comments below.
Good luck and have a great week ahead!
Arpit 🤝
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