4 Tips on How to Ramp up at Your First Data Analyst Job
If you’re reading this, then congratulations! You read all of the Medium articles on how to learn SQL, Python, R, etc. The dos and don’ts of the data analytics world, and now you’ve landed your first data analytics job.
That’s a great accomplishment brought on through a lot of hard work and patience, but now you’ll need to utilize a different set of skills to ramp up and make the most of this new opportunity, and get over the impostor syndrome we all feel when entering a new and unknown space.
Here are the four quick tips on ramping up at your new job.
1. Start connecting with people at work
I felt very lucky when I started my first data analyst job because instead of throwing me in the deep end without my pool floaties, my boss set my first task to set-up meetings with key people within the company. This got me in contact with all the right people at the get go. If I had the simplest questions on how to connect to a particular data source, or what’s the best way to join two tables, I already had a starting point.
If your boss hasn’t given you the assignment to connect with certain people, ask them which people you should connect with on your very first day. If you need to be more explicit, ask for the following type of people:
Analysts who have worked with many of the same or similar tables and databases that you will be working on.
Engineers who are familiar with how the schema and tables have been built.
An employee who has exemplary delivery of reports, dashboards, and other documentation.
An employee who is known to be well connected or very gregarious within the company.
Networking with these individuals will help you cover your basis to a lot of questions you may have when starting a new job. Everything from best practices, system permissions and set-up, and how to further connect with others who can help.
When I start these meetings, I keep them short, 30 mins tops. I first start by providing a quick introduction, when I started, and what my role consists of, and then I ask them to share their role and how you can help them with the job that they do. This creates a mutual understanding that although you are new, you are willing to help and share information where possible. This goes a long way in building strong relationships in the workplace.
2. Know your customers
By customers I don’t mean the people your company sells to. Your customer is anyone who you have to deliver a product or service to. Monthly KPI reports to the VP? The VP is your customer.
Knowing your customer is crucial in any job. Your success is determined on how well you deliver your product to your customer.
Your customer could be your boss, and it could also be someone from another department. For an example, I have to calculate our teams spend and submit it to accounting at the start of every month for them to calculate their accruals. Therefore, I see them and treat them as my customer. I make sure to understand what they want, how they want it, and when. I do what I can to keep my customer happy.
It’s important to identify and connect with your customers early on. In the beginning it may not seem obvious that you have other customers outside of your team, but due to your access and proximity to data, you’ll end up with some ancillary tasks which you’ll have to support. It’s best to get ahead and recognize these customers and tasks, so that when you plan your work load you factor in the time associated with delivering to all of your customers.
3. Learn your industry
Knowing how to access and interpret data is the first step of a data analyst. Making full use of the data you have to understand the industry and what makes it move is your next step. Yes, your manager and team will help you get up to speed in understanding what the company is about, their revenue model, etc. It’s up to you to dig deeper to find out the industry standards used, what are the market factors that can affect your industry, laws and regulations that govern your companies activities.
A great first step, if you’re working for a public company, is to go through the companies annual and quarterly filings, also known as the 10-K and 10-Q respectively. Here’s an example of Google’s 2019 10-K. Do you need to read it word for word? No, but you should read most of part one to get a better understanding of your companies goals and risks, and reading the financials will give you a good idea of the metrics deemed important for your industry.
If your company is not public, try to find a related company that has gone public. For example, Squarespace isn’t a public company, but their competitor Wix is. You can look at Wix’s 10-K to get an idea of what metrics are important to the industry.
Learning this information is vital for how you perform your analytics and on how you present your findings. It’s an immense value add if you can tie your findings in a way that really takes your company, industry, and market into consideration.
4. Write a plan to shore up your technical skills
I landed my first data analyst job by learning SQL and gaining experience with whatever small SQL related work I could find. Landing this job was a giant first step, but there are many more steps to take. If you want to grow as a data analyst then you need to set a road map on what to learn next, and how to apply what you have learned. For the best results take a few months on the job and take notes on how things can be improved. Is there reporting that can be automated? Are there requests that you get often which can be turned into a self-service dashboard? Can all of your data sources be verified for completeness and accuracy if someone comes to ask you to prove your work? See what’s relevant to your job right now, and then based on that make a learning plan to help cover those needs or nice-to-haves.
Your work doesn’t end with your day to day, you need to continue to learn, apply, and grow to get past the feelings of impostor syndrome. If SQL is the only thing you’ve learned so far, here are some ideas of subjects to tackle next:
Analytical/Statistical modeling with Python (learning Pandas and NumPy libraries)
Data visualization tools such as Tableau and Looker
Data visualization scripts for Python with MatPlotLib and Seaborne
Introductory courses to Machine Learning
Applied statistics courses at your local community college
The first step is always the hardest, and now you’ve made it over your first hill. You have the knowledge and experience to go further and you have nothing to hold you back. Keep learning and keep growing. I wish you the best!
Happy learning! 📚
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