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Key Takeaways
- Data analysts play a critical role in business success, turning raw data into actionable insights that improve decision-making, efficiency, and profitability.
- Hiring remotely expands the available talent pool and lowers costs, with nearshoring to Latin America providing skilled professionals who work in aligned time zones.
- A structured hiring process sets you up for long-term success—define business needs, screen for key skills like SQL and Python, test candidates with practical assessments, and onboard effectively with clear 30/60/90 day plans.
Every day, businesses generate massive amounts of data, from customer purchases to website clicks and supply chain updates. But without the right expertise, all of that information is just noise.
Companies that rely on data to drive decisions need to hire a data analyst. These professionals sift through complex datasets, identify trends, and turn raw numbers into clear, actionable insights. However, finding the right analyst is not always straightforward. Do you need someone in-house or remote? What skills actually matter? How much should you pay?
This guide walks you through the entire hiring process, including where to find top talent and why hiring outside the US could be a valuable approach.
What Does a Data Analyst Do?
A data analyst takes raw data and turns it into valuable insights that help businesses make smarter decisions. From identifying trends to optimizing operations and improving customer experiences, they play a key role in driving profitability.
While “data analyst” is the most common job title, you’ll often see these professionals listed also as:
- Quantitative Analyst
- Reporting Analyst
- Analytics Consultant
- Data Consultant
- Data Specialist
- Data Associate
These titles have some nuances, but we’ve seen some companies that use them interchangeably. In general, they refer to professionals who manage data collection, organization, and interpretation as part of a larger analytics function.
Whether you’re looking for a junior data analyst to generate reports or a senior data analyst to guide business strategy, hiring someone with strong technical skills and problem-solving abilities is crucial to making data-driven decisions.
Types of Data Analyst Roles
Data analysts are essential across industries like tech, finance, healthcare, and retail, where companies rely on data to make informed decisions. Are data analysts in demand? Absolutely, demand for data experts is expected to grow 25% between 2023 and 2027.
It’s important to understand the different types of analysts and their areas of focus.
Here are the common specialized types of data analysts:
- Business intelligence (BI) analyst: A BI Analyst creates dashboards and reports to support strategic decision-making.
- Marketing analyst: Studies consumer behavior, campaign performance, and market trends to refine marketing strategies.
- Operations analyst: Examines internal processes to improve efficiency and reduce costs.
- Financial analyst: These professionals focus on financial forecasting, risk assessment, and investment insights.
- Business analyst: Translates data insights into business strategies, focusing on process improvements.
You’ll also find data analysts who specialize in specific sectors like healthcare analytics, sports data, or risk and fraud detection—each bringing industry-specific knowledge to their analytical work.
Essential Skills to Look for in a Data Analyst
Hiring the right data analyst means finding someone with both technical expertise and strong problem-solving abilities. These are some key skills a data analyst needs that employers should prioritize.
Technical must-haves (hard skills)
A junior data analyst may only need basic proficiency in these skills, while a senior data analyst is expected to have advanced expertise:
- SQL: Essential for extracting, managing, and analyzing large datasets. Knowledge of MySQL, PostgreSQL, or Microsoft SQL Server is valuable and the most in-demand skill in the data science sector.
- Python or (less commonly) R: Used for statistical modeling, automation, and machine learning. If your project involves ML or AI and you need Python analysis, you should also be asking them about their experience with pandas, NumPy, and seaborn.
- Data visualization: Ability to create clear, actionable reports using Power BI, Tableau, or similar tools.
- Excel and Google Sheets: Advanced knowledge of formulas, pivot tables, and automation is a must.
- Mathematics and statistics: A strong foundation in probability, regression, and hypothesis testing.
Soft skills that matter
A data analyst with strong communication and collaboration skills is just as valuable as one with technical expertise. You should look for:
- Critical thinking: Ability to challenge assumptions and validate findings.
- Attention to detail: Small data errors can lead to major business mistakes.
- Communication and collaboration: Analysts must translate raw numbers into clear insights for decision-makers.
- Problem-solving: Identifying trends and connecting data points to real business challenges.
Nice-to-have skills
These skills are useful but not always required:
- Microstrategy: A common tool used by large enterprises.
- Cloud-based data tools: AWS, Azure, and Google Cloud experience can be an asset.
Where to Find Skilled Data Analysts
Hiring the right data analyst starts with two key decisions: where to hire and how to source candidates. Businesses must determine if they need an in-house employee, a remote team member, or a freelancer. Each option has advantages depending on budget, business needs, and hiring strategy.
Deciding between local or remote hiring
Choosing between local and remote hiring is one of the most important decisions when looking for a data analyst, as it impacts cost, availability, and hiring flexibility.
In-house hiring
Local hiring allows for face-to-face collaboration but comes with a smaller talent pool and higher salary expectations. With 70% of large companies stating there’s a skill gap in the US, many companies struggle to find qualified candidates in their area. This can lead to longer hiring cycles and increased competition for top data talent.
Remote hiring
Remote hiring, on the other hand, expands access to global expertise while often reducing costs. Many businesses now hire data analysts remotely, sourcing talent from strong data hubs like Latin America and Eastern Europe. These regions offer highly skilled professionals at competitive rates compared to the US market, making them attractive alternatives to hiring solely within the US.
This hiring model offers more flexibility and cost savings but requires strong remote management practices.
Best hiring methods
Once you’ve decided on local vs. remote hiring, the next step is choosing how to find the right candidate:
- Freelance platforms: Websites like Upwork, Toptal, and Fiverr allow businesses to hire analysts for short-term projects. While convenient, freelancers may not offer long-term stability.
- Job boards: Posting on LinkedIn, Indeed, or Glassdoor gives access to a large talent pool but requires time-consuming screening.
- Recruitment agencies: The top agencies provide pre-vetted data analysts, reducing hiring risks and streamlining the process.
What You Should Expect to Pay
Salaries for data analysts can vary widely based on location, experience, and specialization. Hiring within the US typically comes with higher costs, while nearshoring to Latin America, for instance, offers significant savings without compromising on talent quality.
The salary range for a data analyst in LatAm is $30,000–$60,000. In contrast, the average annual salary in the US is $49,000–$106,000. So companies can see a 42% savings by hiring in LatAm.
If you need to build a whole data team, the savings will increase.
Hiring a BI analyst, data analyst, data engineer, and data scientist could cost $282,000–$589,000 per year in the US.
Hiring the same team in Latin America would cost $138,000–$264,000, cutting expenses by over 50% while still securing highly qualified professionals.
These cost differences reflect the lower cost of living in LatAm countries, not a difference in talent quality. LatAm professionals often earn competitive local salaries while US companies still realize significant savings. This creates a win-win situation where businesses reduce costs, and LatAm data analysts earn above their local market rates.
For a full breakdown of expected salaries for data roles in LatAm, you can explore our salary guide.
The Step-by-Step Guide to Hiring a Data Analyst
Hiring a data analyst requires a structured approach to get the right fit for your business. Beyond technical skills, consider your specific data needs, hiring methods, and onboarding process.
Step 1: Define your business needs
First, determine what kind of data analysis your business requires. Different departments need different specializations—marketing teams need customer segmentation and campaign tracking, finance requires forecasting and risk assessment, while operations benefits from process efficiency analysis.
Decide what your business needs and create a detailed data analyst job description. Make sure it covers everything needed to attract the right candidate.
Step 2: Decide on remote vs. in-house hiring
Determine whether your data analysis needs require an on-site presence or can be handled remotely. Consider your budget constraints, timeline, and security requirements when deciding.
Step 3: Choose the right hiring method
Select your candidate sourcing strategy: recruitment agencies for pre-vetted candidates, job boards for a broader reach, or freelance platforms for project-based work.
Step 4: Screen & test candidates
Start with an initial resume screening to identify candidates with the right technical background and experience. Look for proficiency in SQL, Python, and data visualization tools, along with relevant industry experience.
For the most promising candidates, conduct a brief technical screening call to verify skills before investing in comprehensive assessments.
Only then move forward with a practical test project that mirrors the actual work they’ll be doing, evaluating both technical ability and analytical thinking. This staged approach ensures you focus your time on truly qualified candidates.
(However, a recruitment or staffing firm often handles much of the evaluation process for you.)
Step 5: Conduct interviews for short-listed candidates
Focus interview questions on both technical capabilities and communication skills. Ask candidates to explain past data projects and how they’ve translated findings into business impact.
Have them walk through their process for tackling a complex dataset—from cleaning and organizing to visualization and presenting insights.
Pay attention to how clearly they explain technical concepts to non-technical stakeholders. Include team members who’ll work directly with the analyst in the interview process to ensure cultural fit and collaborative potential.
Step 6: Make an offer & onboard effectively
Finalize salary terms, work expectations, and project timelines before sending a formal offer.
When onboarding your new data analyst, provide access to relevant databases, visualization tools, and documentation within the first week.
Introduce them to key stakeholders across departments who can explain business context and data needs.
Set clear expectations for their first 30/60/90 days, including specific projects and learning objectives.
Schedule regular check-ins during this period to address questions and provide feedback, ensuring they integrate smoothly with your existing processes and team.
Final Thoughts
Hiring the right data analyst can transform how your business operates. When you find someone who can turn your raw data into actionable insights, you’ll make better decisions, spot hidden opportunities, and solve problems before they become expensive mistakes. But the reality is that finding this talent locally is becoming increasingly difficult—and prohibitively expensive for many companies.
If you’re like most hiring managers we talk to, you’ve already spent months trying to fill a data analyst position without success. The candidates either don’t have the right skills, want salaries that break your budget, or get snatched up by competitors before you can make an offer.
That’s where Near can help. We connect US businesses with top-tier data analysis talent from Latin America. These professionals have the technical skills, English proficiency, and business acumen you need—and they work during your business hours for real-time collaboration.
By working with Near, you’ll typically hire in under 21 days and save 30–70% compared to US salaries without compromising on quality.
Schedule a free consultation today to see how we can help you build your data team.
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