Data & Analytics Manager: "SQL is More Important Than Python To Me"
Douglas Robertson
Recovering data analyst, helping businesses hire top data and analytics talent | Founder @ DR Analytics Recruitment | Head of Operations @ Data Engineer Camp | Perth 📍
I recently had a conversation with a Data & Analytics Manager who was gearing up to hire a graduate for his team.
We both noted that there has been a significant influx of graduates eager to break into the field, particularly those with skills in Data Science and Python.
As we discussed the current landscape of talent, he confessed that SQL was a far more important skillset than Python for his team and felt that the current graduate group was extremely data science heavy and very eager to work in Python!
However, this manager highlighted a gap in the market—while many job seekers have impressive programming skills and can build a model to predict a business outcome in a few days, they may lack strong foundations in SQL which are essential for success in real-world data environments.
Within his team, the skillsets in demand were data visualisation and database development as they have contribute to real business value.
Priority attributes for a graduate:
SQL was the most important programming language.
In fact, he felt that for his team and the company's data maturity, they would not be able to provide the professional satisfaction for someone who wanted to use Python to build data science use cases - there just wasn't the need or business appetite.
This may be a reflection of Gartner's comments where they predicted in 2018 that by 2022 85% of Data Science/AI projects would fail meaning there isn't as much business appetite!
The Reality of SQL Pipelines
The manager’s focus was clear: he was seeking candidates who possessed a solid grasp of SQL, which is fundamental for building data pipelines. In his view, SQL is the backbone of any data-driven operation, enabling teams to extract, manipulate and manage data effectively.
For this business, the truth is, while Python and advanced analytics techniques are undoubtedly valuable, the core competencies in SQL and data visualisation are what would drive meaningful insights and business value. Graduates with a strong understanding of these foundational skills are better equipped to navigate the challenges of the business.
Recommended by LinkedIn
Steps to Upskill Effectively
Now the challenge - how do you show this as a job seeker or identify skills as an employer:
Here are some practical steps you can take to enhance your skills and make yourself a more competitive candidate in the data and analytics field:
Noting here that this is only one company and one hiring manager's opinion. There are other domains that find Python to be in high-demand over SQL! Sharing this as an anecdotal insight into the market for job seekers or even employers!
DR Analytics Recruitment
I'm Douglas - former data analyst and Founder of DR Analytics Recruitment. We grow people and businesses with an exclusive focus on the recruitment of data, analytics and AI professionals. Companies use us to build their data teams because of our industry expertise, specialisation and technical testing.
Get in touch to learn more!
📧 Email: douglas@analyticsrecruitment.com.au
📞 Phone: +61 430 846 876
Software Engineer, Doctor of Information Technology
3moLisa Barbosa - We spoke about SQL the other day. 😀
Totally agree with this! SQL is such a core tool and I use it every day when working, from quick queries to more complex data work. Python and data science are great, but they don’t always fit every case, especially when it comes to quick, ad-hoc analysis. Nowadays, I feel like both SQL and good old spreadsheets can get overshadowed by the hype, so it’s awesome to see this being highlighted. :) Thanks for sharing!
The guy from Special Circumstances | I fix Tech organisations
3moAnd spreadsheets, data is rarely clean, and often a spreadsheet is the quickest path to work out what needs to happen for cleansing.
CEO at RadiXplore | Geoscientist | Data scientist
3moUniversities and online courses often lack emphasis on applying theoretical knowledge to real-world industrial applications, particularly when working with large datasets. In practice, these datasets are typically stored in data warehouses, commonly in SQL databases. To perform any meaningful data science work, you would first need to carry out ETL (Extract, Transform, Load) processes. SQL databases are optimized for these types of calculations, which is why having a solid understanding of SQL is crucial in these scenarios.