Data Analyst

A data analyst specializes in the cleaning, processing, analysis, and visualization of datasets. They are generalists, which means they can come from many different backgrounds, but there are a few core traits that they are expected to apply on the job.

What this role is:

Data analysts are coders. They are nearly always expected to be able to write queries in SQL (a database querying language), and are generally expected to be proficient in at least one programming language (usually, Python or R).

Data analysts are versatile. They are expected to be capable of working with a variety of types of data and often are expected to learn on the job. They are also expected to approach ambiguous data analysis tasks with curiosity.

Data analysts are communicators. They are expected to clearly and effectively report on findings from their analyses to a variety of stakeholders. Some data analysts (but not all) are even expected to communicate their findings directly to external clients.

What this role is not:

Data analysts are not expected to be software engineers. They are generally expected to write code, but they are not expected to build applications, design software architecture, or engage with complex data structures.

Data analysts are not expected to be machine learning experts. They are expected to be able to apply basic statistical analyses to datasets, but they are not expected to create, tweak, or evaluate the performance of machine learning algorithms.

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Data Engineer / Architect A data engineer or architect is a specialized version of a software engineer. Data engineers build software architecture to support the transmission of data across software systems. They are sometimes additionally expected to perform data analyses, but their primary responsibility is building and maintaining codebases.

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Data Scientist A data scientist is essentially an advanced data analyst. Data scientists specialize in using complex statistics and machine learning to run experiments and glean insights from large datasets. They generally have specialized, advanced statistical education.

Though these are all distinct roles, there is some overlap in their responsibilities, which means that some of the information in this guide may be useful regardless of which data-related role you are hiring for. Most notably, all of the skills and strengths that are expected of Data Analysts are also expected of Data Scientists, since Data Scientists are essentially advanced Data Analysts.

Skills + Strengths Checklist

“What are the skills and strengths that I should be looking for in Data Analyst candidates?”

Through the research we’ve conducted at Byteboard, we’ve come to think about a candidate’s proficiency for a role in terms of skills and strengths. A strength is a broad competency that can be applied across a variety of domains (e.g. attention to detail, reading comprehension, or task prioritization). A skill is the understanding of a particular domain or toolset (e.g. SQL databases, QA testing, or statistical analysis), where a strength can be applied.

Below is a list of the skills and strengths that our research has found to be most important for Data Analysts.

Core Skills

Data Cleaning + Processing: SQL

Data Analysis: Two or more of Exploratory Data Analysis (EDA), Confirmatory Data Analysis (CDA), basic data mining

Statistical Programming Languages: R or Python

Data Visualization: Tableau, PowerBI, Python, or R

Bonus Skills

Advanced Statistics / Machine Learning (e.g. statistical modeling, regression analysis)

Advanced Excel

Experiment design

Continuous integration / continuous delivery (CI/CD)

Core Strengths

🧩 Pattern Recognition

📌 Attention to Detail

🤖 Technical Reasoning

📝 Written Communication

🤝 Collaboration

⏩ Adaptability

Bonus Strengths

🖼️ Visual Communication

📏 Edge-case thinking

📖 Information Gathering