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Tips for Hiring Data Analysts

No matter what your company creates or sells, you need to know your target audience's demographics and habits. You also need to understand current trends in your industry. Without this data, you'll have a hard time determining the best marketing and business strategies for your company.

That's where data analysts come in. As seasoned information technology (IT) professionals who gather, clean, and study data, data analysts can help you answer questions about your industry, company, and customers. For example, they can help you discover:

  • The age range of customers for a particular product
  • Which customer behavior patterns are connected to higher conversion rates
  • Which social media platform has the highest conversion rate and why

However, hiring the right data analyst for your company remains a challenge. Although there's a glut of data analysts on job sites like Indeed, many lack the skills and experience to help your company succeed.

Read our comprehensive guide to learn how you can hire data analysts. We'll cover what data analysts do, how they differ from data scientists, how they can help you reach your business goals, and how to hire one. We'll also provide tips for writing compelling data analyst job descriptions and interview questions.

What Is a Data Analyst?

A data analyst is an IT professional who collects and interprets data to solve specific problems. After they've thoroughly analyzed the data, they will communicate their findings to the rest of your team.

Although data analysts and data scientists both analyze data to understand your company and industry, they have different approaches. Data analysts examine data to understand the past better, while data scientists analyze data to make assumptions about the future.

Other differences include:

What Do Data Analysts Do?

A data analyst's duties vary depending on the company, but most companies require data analysts to:

  • Gather data: Analysts usually gather data themselves. They do this by tracking visitor demographics on your website and conducting customer surveys. Some may also buy datasets from data collection companies or specialists.
  • Clean data: Raw data usually contains outliers, duplicates, and errors. As such, analysts need to maintain data quality by using a programming language or spreadsheet. Otherwise, the interpretations may be inaccurate.
  • Model data: Data analysts create and design the structures of the database. They choose what kinds of data to collect, establish how categories are related to one another, and determine how the data will be displayed.
  • Interpret data: Data analysts look for patterns and trends in the data to answer questions about your company, user base, and industry.
  • Present findings: After interpreting the data, data analysts will communicate their findings to the rest of the team. They will use visualizations like graphs and charts and produce reports to make the information easier to digest.

Analysts must know the following tools, concepts, and skills to perform their duties:

  • Database tools: Structured query language (SQL), Google Sheets, and Microsoft Excel should be part of every data analyst's toolbox. SQL is a domain-specific language used to manage data in relational database management systems. It can handle larger datasets than Excel and Google Sheets and is widely seen as a necessity for data analytics.
  • Data visualization: Data analytics experts should also know how to use data visualization tools like Jupyter Notebook, Microsoft Power BI, Tableau, and Excel. These tools allow users to present findings in accessible formats like graphs and charts.
  • Programming languages: Data analysts are experts in statistical programming languages like Python and R, which they use to handle large datasets and perform complex calculations.
  • Mathematics and statistics: Last but not least, data analysts should have a solid grasp of math and statistics. This will help them:
  • Pick the best tools for solving certain problems
  • Catch errors quicker
  • Get a better grasp of the results.

Why Should You Hire a Data Analyst?

Having a skilled data analyst comes with a plethora of benefits. Here are the main reasons for hiring one:

  • Understand user demographics: Data analysts can look at your website and social media traffic and identify which groups of users are more likely to buy your products. This can help you target users more effectively and efficiently. For example, suppose your data analyst discovers that users from Chicago, Illinois, are more likely to buy your laptops than users from Columbus, Ohio. In that case, you should consider targeting more ads toward Chicago-based users.
  • Streamline your decision-making process: Experienced data analysts can look at historical data to see the effects of past decisions. This will help you understand which decisions work and which don't.
  • Find solutions to business and marketing problems: Data analysts can look at datasets to identify solutions to business and marketing questions like:
  • Which of our products have the most negative reviews? Why are these products unpopular? How can we improve these products?
  • Which social media platform is the most effective for video marketing?
  • Which department or team is falling behind and why?
  • Evaluate internal systems for problems, efficiency, and inaccuracies: Data analytics professionals can also evaluate and improve your internal systems, tools, and protocols.
  • Promote a data-centric work culture: Data analysts can help you create a data-centric work culture. They can teach other team members how to use and process data and what to do with their insights.

How To Hire a Data Analyst

Hiring a data analyst can present many advantages, including a better understanding of your company, industry, and past decisions. A skilled data analyst can also help you evaluate existing systems for efficiency, problems, and inaccuracies and promote a data-centric work culture.

However, before you start hiring, you need to ensure that your company is ready for a data analyst. You also need to consider prospective hires' skills, qualifications, experience levels, and salaries.

Ensure That Your Company Is Ready for a Data Analyst

First, you need to determine whether your company is ready for a data analyst. Specifically, you need to evaluate your company's:

IT Infrastructure

If your IT infrastructure isn't set up properly, data analysts won't be able to do much. So make sure your IT infrastructure can do the following before deciding to hire a data analyst:

  1. Collect data: Your software engineers should set up data collection tools and systems for data analysts. Depending on your industry and what information you want to gather, these tools and systems can range from device sensors and website traffic monitoring tools to surveys.
  2. Move and store collected data: Data should also flow reliably through your IT infrastructure. If it doesn't, data analysts will have a difficult time analyzing and using your data. Analysts need interconnected IT architecture to update datasets with new data. Common options for storing data include:
  1. On-premise data storage: This involves storing data on servers owned and managed by your company. On-premise data storage gives you the greatest amount of control over your data and network, but it also requires you to build and maintain IT infrastructure.
  2. Hosted data storage: This storage method involves storing data off-site on cloud providers such as Microsoft Azure and Amazon Web Services. Besides managing the whole storage process for you, these solutions also provide large tech stacks that you can use to turn your data into meaningful products, insights, and services. You can also choose to store data at local data centers.
  3. Hybrid data storage: Hybrid data storage uses on-premise storage for sensitive data and hosted data storage for non-sensitive data.

Data Culture

Next, you need to examine your company's data culture. Does your company have a strong data culture? Companies with strong data cultures:

  • Regularly use data to make decisions.
  • Treat data as the main way of getting business insights.
  • Teach staff to access, use, and analyze data.

Amount of Data

You also need to look at how much data your company has. If you only have small datasets, you don't have to hire a data analyst. Data analysts need lots of data to derive useful insights about your company.

Type of Data

Finally, you need to consider the type of data in your databases.

If your data is structured and only comes from a single source, a data analyst is a good pick for your team.

However, you should consider hiring a data scientist if your data is mostly unstructured and comes from various sources. As mentioned previously, data analysts typically don't have the skills or experience to work on unstructured datasets from different sources, since these datasets require expert knowledge of SQL, databases, ML, and AI.

Identify Essential Data Analyst Skills

Once your company is ready for a data analyst, it's time to make a list of essential data analyst skills. These include:

Hard Skills

The best data analyst for your startup should have the following hard or technical skills:

Data Visualization

Data visualization is the ability to present data findings through illustrations, graphics, and other visuals. The ideal data analyst should know how to use data visualization tools to explain data-driven insights to technical and non-technical audiences.

With data visualization, data analysts can help C-level executives and the marketing department spot patterns and understand complex issues at a glance.

Data Cleaning

Your hire should know how to clean data. Cleaning datasets will make extracting and analyzing insights easier. On the other hand, uncleaned data can produce confusing patterns and lead your company towards wrong conclusions.

Mathematical Skills

The best data analyst for the job should boast advanced mathematical skills. Two specific mathematical fields that are particularly important in analytics are calculus and linear algebra. Calculus is used to build cost, objective, and loss functions that train algorithms to reach their goals. Linear algebra, on the other hand, has applications in ML and deep learning, where it supports tensor, vector, and matrix operations.

Programming Skills

Like many other IT professionals, data analysts should have robust programming skills. Here are the top programming languages that data analysts should know:

  • Python: This general-purpose programming language enjoys immense popularity among IT professionals. It offers a staggering number of unique libraries, including ones for AI. Your hire should also know the following Python-adjacent programs:
  • NumPy, a package that assists Python users with scientific computing tasks
  • Pandas, an open-source data analysis tool
  • R: Designed to support analytics, R can handle large datasets and provides several easy-to-use data organization commands. It's also one of the most well-known data analytics languages.
  • SQL and NoSQL: Your hire should also know how to use SQL and NoSQL. SQL is the standard way to query and manage data in relational databases, while NoSQL is the non-SQL way to organize datasets. NoSQL frameworks can structure data in any format as long as the method isn't relational.
  • MATLAB: A multi-paradigm computing environment and programming language that supports matrix manipulations, algorithm implementation, and data plotting, MATLAB facilitates quick data organization, cleaning, and visualization. It also reduces time spent on pre-processing data.
  • Java: A general-purpose language that runs on the Java Virtual Machine (JVM), Java offers frictionless portability between platforms. It also provides tools for integrating data science and analytical methods into a codebase.
  • Scala: A programming language with object-oriented and functional approaches, Scala is essentially an updated version of Java. As such, it can run on JVM. Many analysts use Scala with Apache Spark to handle massive datasets.

Soft Skills

Besides hard skills, your data analyst should also boast well-honed soft skills like communication skills, problem-solving skills, teamwork skills, and attention to detail. These skills don't require certification or training, but they have a large effect on whether your hire is a good fit for your company.

Here are the top soft skills you should look for:

Communication Skills

If data analysts don't have the communication skills to present their findings effectively, their reports and insights can't help anyone. Knowing how to use Excel, SQL, and other tools isn't enough — data analysts need to know how to effectively summarize, present, and explain concepts and insights to technical and non-technical staff.

Problem-Solving Skills

Data analysts must know how to solve problems when they crop up. They need to know how to stay calm and troubleshoot problems so that they can continue to look for answers. Your hire should also know how to collaborate with other teams and departments to solve big-picture issues.

Teamwork Skills

Your hire should also know how to work with other departments and teams to get the job done. They should know how to work with the following:

  • Data scientists: Data analysts should know how to work with data scientists to determine what kinds of questions can be answered via data analysis.
  • Web developers: Analysts need to collaborate with web developers to ensure that the company site can capture the data they need.
  • Company leaders: Like data scientists, data analysts need to collaborate with company leaders to determine how data insights can help your business reach its objectives.
Attention to Detail

The ideal data analyst should have an eye for detail. After all, they're looking at large datasets to locate small clues that point towards larger conclusions.

Research Skills

Data analysts need strong research skills to make sense of the information they've collected. They also use research to stay on top of company and industry trends so that they can get relevant insights from their data. Research is also useful when analysts are presenting their findings to management and defending their position on what the company should do next.

Business Acumen

Data analysts also need to have sharp business acumen to create actionable insights for your company. Specifically, they need to know:

  • How decisions and actions affect key company measures and goals.
  • How data-driven insights can support future success and growth.
  • The unique needs of your industry and business.
  • How to transform data into insights and results.

Learn More: Data Warehouse as a Service: How Does it Work?

How Much Do Data Analysts Make?

Once you have a list of required hard and soft skills for data analysts, you need to think about salaries. A data analyst salary depends on skill and experience level, so let's look at the skills and expected salaries of entry-level, junior, and senior data analysts.

Learn More: Business Intelligence vs. Data Analytics: What's the Difference

Entry-Level Data Analyst Skills and Salary

Entry-level or beginner data analysts have zero to three years of experience. The vast majority are fresh grads with bachelor's degrees in Computer Science, Data Science, Mathematics, Statistics, or other related fields. Some may be recent graduates of boot camps or self-taught.

Since these data analysts have little to no relevant work experience, they have fewer skills and lower salaries. To help them reach their potential, consider giving them mentorship and educational opportunities.

According to Glassdoor, the average U.S.-based entry-level data analyst makes $64,051 per year.

Entry-level data analysts should have the following skills:

  • Zero to two years in marketing and business analytics
  • Experience with Python, R, SQL, or equivalent
  • Flexible
  • Detail-oriented
  • Passion for emerging analytics and media tools

Junior Data Analyst Skills and Salary

Junior data analysts have over two to four years of experience. Since they have a few years of real-life experience, you can expect more from them.

According to Glassdoor, the average U.S.-based data analyst earns $69,517 per year.

Junior data analysts are expected to have the following skills:

  • Exceptional verbal and written communication skills
  • Strong interest and knowledge in finance
  • Proven experience maintaining relationships with stakeholders
  • Two years of experience with SQL database management systems like Oracle and Microsoft SQL Server
  • Two years of experience with NoSQL database management systems like Cassandra and MongoDB
  • Proficiency with Excel functions and formulas like Index Match and VLOOKUP, conditional formatting, and pivot tables
  • Knowledge of data cleansing, de-identification, and data masking
  • The ability to thrive in a deadline-driven environment
  • Strong teamwork and research skills

Senior Data Analyst Skills and Salary

Senior data analysts have over four years of professional experience. According to Glassdoor, the average U.S.-based senior data analyst earns $96,809 per year.

A senior data analyst should have the following skills at a minimum:

  • Proficiency in SQL and NoSQL
  • Experience working with large datasets from different sources
  • Using various ETL tools like Azure Data Factory and Alteryx
  • Experience with cloud technologies like Azure
  • At least three years of experience with Business Intelligence (BI) tools like Tableau
  • Knowledge of various software lifecycle development lifecycle (SLDC)
  • Experience working in various development frameworks, such as Waterfall, Agile, and Kanban

Write a Compelling Data Analyst Job Description

Next, you need to write a clear and compelling data analyst job description to attract leading-edge talent. At a minimum, your job ad needs to cover the following:

  • Job title
  • Company description
  • Responsibilities and duties
  • Required experience and skills
  • Compensation and benefits
  • Working schedule and location

Remote Senior Data Analyst  — Revelo

Revelo is looking for a remote Senior Data Analyst to join our team.

You will be joining a highly collaborative Agile team. Although this position is fully remote, you can work in hybrid mode if you're located in San Diego, CA, where our U.S. head office is.

This role is open to Senior Data Analysts in the following time zones:

  • Pacific Standard Time (PST)
  • Central Standard Time (CST)
  • Mountain Standard Time (MST)
  • Eastern Standard Time (EST)

Company Description:

At Revelo, we use the latest analytic processes and tools to maximize our offerings and deliver top-notch customer service and support. Our mission is to match startups around the world with FAANG-calibre developers from Latin America.

Learn more about us on our website, www.revelo.com.


  • Work closely with management and marketing to identify Key Performance Indicators (KPIs) and critical metrics and deliver actionable insights to decision-makers
  • Create and maintain visualizations for data-driven insights
  • Create and manage data-related artifacts such as source-to-target mappings and data flow diagrams
  • Create and manage logical and conceptual data models
  • Work in an Agile environment
  • Give regular updates on project status to project managers
  • Collaborate with both technical and business stakeholders
  • Proactively analyze data out of self-initiated curiosity
  • Sharp business acumen and understanding of what drives business performance
  • Develop and maintain SQL and NoSQL databases by acquiring data from various sources
  • Build scripts that will make our data analysis process more scalable


  • At least four years of experience as a data analyst
  • Proven analytic skills, including data mining, analysis, and visualization
  • Extensive experience with R, Python, Scala, and Java
  • Strong Excel, SQL, and non-SQL skills
  • At least three years of experience presenting reports, queries, and representations

Nice-to-have skills:

  • Strong programming experience with frameworks like JavaScript and XML
  • Proven ability and experience in statistical analysis through Excel, statistical packages, and SAS
  • Prior experience working in a remote or hybrid environment


  • Competitive base salary of $96,000 to $105,000 depending on experience
  • Dental and medical insurance
  • Parental leave
  • Six weeks of paid vacation


  • 8:30 AM to 5:30 PM PST
    Monday to Friday

Create Data Analyst Interview Questions

The last step is to create thoughtful and informative data analyst interview questions to help assess a candidate's technical and soft skills, and their cultural fit within your company.

Many companies ask academic questions like "Define data science" and "How does SQL work?" These questions can show you how well an applicant knows their stuff, but they don't say much about the applicant's experience, work ethic, and personality.

Consider personalizing your questions to get a fuller understanding of a prospective hire and what they can do for your company. Here are some examples:

  • What are your favorite statistical analysis tools and database software and why?
  • Tell me about a time when you demonstrated good data sense.
  • Describe the most complicated project you've done. What made this project difficult, and how did you handle these challenges?
  • What drew you to data analysis?
  • How would you explain your findings to an audience that doesn't know what a data analyst does?
  • How would you explain your findings to an audience that knows what you do?
  • What are the five best qualities for data analysts?
  • What would you do with missing or inaccurate data?
  • Do you prefer R or Python? Why?
  • How do you handle stress?
  • What's your approach to working on a multi-disciplinary team?

Start Recruiting Data Analysts With Revelo

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Contact us today to start recruiting data analysts.

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