Top 40 Skills and Tools for Data Analysts and the Emergence of the Data Engineering Analyst Roles

ODSC - Open Data Science
6 min readApr 6, 2022

As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently. We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2022.

Data Analytics Skills

The modern data analyst is expected to have a broad range of skills and expertise. Data analysis itself is one of the top-ranked skills, but that’s a broad definition that covers a wide range of topics, such as data wrangling, data science, domain expertise, and more.

Core Data Analytics Skills: Excel, Statistics, Math, Business Analysis, Quantitative Analysis

Most people think that data analytics is a mix of Excel and analytics, and that’s indeed on the money. Excel is the third most sought-after skill in our chart as it’s still an industry standard for data management and analytics. Below that we see various analytics supporting skills, such as statistics, statistical analysis, mathematics, and qualitative analysis.

There’s still a strong demand for the fundamental mathematical skills that contribute to analytics, including statistics, math, business analysis, and quantitative analysis. As you’ll see below, however, a growing number of data analytics platforms, skills, and frameworks have altered the traditional view of what a data analyst is.

Data Wrangling: SQL, ETL, Databases, Big Data

The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in SQL, databases, and ETL (Extract, Transform, Load) are essential. Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis.

This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. While there’s a need for analyzing smaller datasets on your laptop, expanding into TB+ datasets requires a whole new set of skills and data analytics frameworks.

Domain Expertise; eCommerce, Finance, Healthcare

Employers are looking for people who also know the field they’re working in, as data works best when the context is known. In particular, eCommerce and online retail made the cut as they often involve some of the largest amounts of data compared to other industries. While not noted in the chart, we’ve seen a lot of movement in finance and healthcare as industries where data analysts benefit from some domain expertise.

Data Presentation: Communication Skills, Data Visualization

Any good data analyst can go beyond just number crunching. The chart shows a number of presentation-based skills that will make any data analyst stand out. Being able to not just analyze numbers but also share findings with shareholders will cover more ground. Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story.

Data Science & Machine Learning

There’s an increasing amount of overlap between data scientists and data analysts, as shown by the frameworks and tools noted in each chart. While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, and data mining as additional techniques to help further analytics.

Programming and Data Engineering

Both programming and data engineering made the list to our surprise, as data analysts aren’t generally developers. However, as you’ll see in the next section on data analytics tools and platforms, there’s an increasing need for DAs to be able to work with cloud platforms, data storage tools, and the complete modern data stack. Additionally, there is an emergence of the role “Data Engineering Analyst” that is heavily dependent on these two must-have stills. These individuals will be responsible for preparing and analyzing the data themselves, as opposed to them being separate responsibilities.

Data Analytics Platforms and Tools

The chart above shows a number of data analytics platforms that any aspiring data analyst should know. As you see, there are a number of reporting platforms as expected, but there are also some surprising data engineering platforms that further highlight the emergence of the Data Engineering Analyst as a job as noted. .

Data Analytics Platforms: Tableau, Power BI, Looker, Qlik, Aleryx

The most common trend shouldn’t come as a surprise, as the most in-demand data analytics platforms all revolve around reporting, such as Tableau, Power BI, Looker, Qlik, and Alteryx. These are powerful tools that can connect directly to data sources, can run complex analyses, and can output intricate dashboard reports.


Excel’s still needed, even as the lines between data science and data analytics begin to blur. Despite the sheer amount of new tools and the growing prevalence of big data, Excel still sits near the top. A significant amount of basic data analytics is still done in Excel, so being a spreadsheet guru is still attractive to employers.

Data Engineering Analytics: Python, R, Go, SAS

The chart points to the emergence of the Engineering Analyst role. These analytics skills have traditionally been associated with data engineers, but the need for individuals to both process and analyze data is becoming more apparent. There’s a growing need for data manipulation skills using prominent languages like Python, R, or Go. Statistical Analysis Software has a long history in the DA space and continues to have a large following.

The Modern Data Stack: Spark, Redshift, Bigquery, SQL Server, Oracle

The modern data stack continues to have a big impact, and data analytics roles are no exception. More companies are looking for data analysts that know how to work with columnar platforms like Snowflake and big data systems such as Redshift, Bigquery, and Apache Spark. Given the importance of SQL, then there should be no surprise that Relational Database Management Systems (RDMS) such as Oracle and Microsoft SQL Server are also listed.

Cloud Services: Azure

Cloud-based services are the norm in 2022, this leads to a few service providers becoming increasingly popular. However, in this case, when comparing Microsoft Azure, AWS, or Google Cloud Platform, Azure still seems to be the winner.


No self-respecting data analyst would go anywhere without a presentation deck in PowerPoint. Joking aside, it really does support the fact that making and presenting a good visual presentation remains a core skill.

Learn more about data analytics platforms and skills at ODSC East 2022

We just listed off quite a few data analytics platforms, skills, and frameworks. It’s not expected to know every single thing mentioned above, but knowing a good chunk of them — and how to apply them in business settings — will help you get a job or become better at your current one.

At ODSC East 2022, we have an entire track devoted to data analytics and big data. Learn data analytics platforms and skills like the ones listed above. Here are a few sessions scheduled so far:

  • Using Apache Kafka and Apache Pinot for User-Facing, Real-Time Analytics: Karin Wolok and Tim Berglund | Head of Developer Community/ Developer Relations Advisor | StarTree
  • Next Generation of Distributed Computing with Dask: David Chudzicki | Senior Software Engineer | Coiled
  • Deep Dive Workshop for Apache Superset: Srinivasa Kadamati | Committer, Senior Data Scientist / Developer Advocate, Apache Superset | Apache Superset, Preset
  • Changelog Stream Processing with Apache Flink: Seth Wiesman | Senior Solutions Architect & Apache Flink Committer | Ververica
  • Building and Deploying the World’s Largest Rock/Paper/Scissors Competitive Ladder App in X Minutes with Roboflow and Streamlit: Jay Lowe | Field Engineer | Roboflow
  • Bridging the Gap Between Data Scientists and Business Users: Amir Meimand, PhD | Data Science, ML Solution Engineer | Salesforce
  • A bamboo of Pandas: crossing Pandas’ single-machine barrier with Apache Spark: Itai Yaffe, Daniel Haviv | Senior Solutions Architect, Senior Solutions Architect | Databricks
  • WeightWatcher, an Open-Source Diagnostic Tool for Analyzing Deep Neural Nets: Michael Mahoney, PhD | Statistics Professor, Faculty Scientist | UC Berkeley, Lawrence Berkeley National Laboratory
  • Accelerating Your Advanced Analytics & ESG (Environmental, Social, and Governance) Journey: James Olejniczak | Product Manager of Visualizations, Data Management Solutions | S&P Global Market Intelligence

Original post here.

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