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Technology & Data7 min read

Data Analyst

Data analysts use SQL, spreadsheets, and BI tools to answer business questions with clear, defensible analysis. Expect a lot of cleaning data before you ever build a model.

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What does a Data Analyst do?

A data analyst pulls data from databases, cleans it, analyzes it, and communicates findings to non-technical stakeholders. Entry-level analyst work is usually 60% data wrangling, 30% analysis, and 10% communication — but the 10% is what determines whether you're seen as valuable. Strong analysts make complex queries look simple in their final write-up.

Common responsibilities

  • Write SQL to pull data for ad-hoc business questions
  • Build and maintain dashboards in Tableau, Looker, Power BI, or Sigma
  • Document data definitions and metrics so the team is consistent
  • Investigate anomalies (spikes, drops, weird customer behavior) and explain causes
  • Partner with product, marketing, or ops on experiments and A/B tests
  • Help define KPIs for a team and stand up reporting around them
  • Clean and standardize data feeds from multiple source systems
  • Write short executive-friendly summaries of analysis findings

Skills to highlight on your HireMe profile

Hard skills

  • SQL — joins, group by, window functions, CTEs
  • Excel/Google Sheets — pivots, lookups, basic statistics
  • A BI tool: Tableau, Looker, Power BI, Sigma, or Mode
  • Basic statistics: distributions, sampling, A/B test logic
  • Bonus: Python or R basics for data cleaning

Soft skills

  • Explaining a SQL query in one sentence
  • Writing crisp summaries with a recommendation, not just numbers
  • Pushing back on bad questions and reframing them
  • Catching your own errors before stakeholders do

Tools & platforms

  • SQL flavors: PostgreSQL, MySQL, BigQuery, Snowflake, Redshift
  • BI: Tableau, Looker, Power BI, Sigma, Mode, Hex
  • Notebook environments: Jupyter, Hex, Deepnote (for Python/R)
  • Source data: GA4, Salesforce, HubSpot, Stripe, Segment

Who this role is a good fit for

  • Anyone who has built a personal dashboard, side project, or scraped public data for fun
  • Students who enjoyed stats, econometrics, or experimental research
  • Candidates who write clearly and concisely
  • Future data scientists, analytics engineers, or product managers

Majors and backgrounds that fit

  • Statistics
  • Mathematics
  • Economics
  • Computer Science
  • Business Analytics
  • Information Systems

Common entry-level job titles to search for

Hiring managers use different titles for the same role. When you search job boards or filter on HireMe, try variations like:

  • Data Analyst
  • Junior Data Analyst
  • Analytics Analyst
  • Reporting Analyst
  • BI Analyst
  • Product Analyst

How to make your HireMe profile stand out for this role

  • Build one small portfolio piece: a public dashboard, a Kaggle write-up, a blog post analyzing a dataset. One real artifact beats five certifications.
  • List the SQL flavor and BI tool you've actually used. Recruiters search for these exact names.
  • Show one analysis you did that changed a decision, even a small one (e.g. "recommended switching club Instagram posting time based on engagement analysis").
  • Mention any open-source datasets you've worked with (NYC Taxi, Citi Bike, NOAA, Census).
  • If you have ever cleaned a real messy dataset, describe what you fixed.

Interview preparation tips

  • Expect a SQL test: joins, aggregation, and at least one window function.
  • Be ready to walk through how you'd analyze something like "sign-ups dropped 20% — what would you investigate first?"
  • Practice presenting a chart and saying what you'd recommend in two sentences.
  • Have one example of when your first interpretation of the data was wrong and how you caught it.

Reality checks before applying

  • Entry-level analyst work is mostly cleaning data and answering ad-hoc questions, not building models.
  • Beware roles labeled "data analyst" that are 100% Excel exports with no SQL access — they tend to plateau.
  • Bootcamp certificates alone won't get you hired without a portfolio piece you can talk through.

Frequently asked questions

Do I need a data-science bootcamp to get hired as a data analyst?+
No. What matters is a portfolio piece or two that shows you can actually pull data, analyze it, and write a clear summary. SQL fluency is the bigger differentiator.
What's the difference between a data analyst and a data scientist?+
Analysts focus on querying, dashboards, and clear conclusions. Data scientists go deeper into statistics, modeling, and machine learning. Most teams expect you to be a strong analyst before you do meaningful data science.
Is Python required for data analyst roles?+
Not for most entry-level jobs. SQL is far more important. Python or R helps later, especially for analytics engineering or product-analytics work.
Are remote data analyst jobs realistic for new grads?+
They exist but are more competitive. Hybrid roles tend to be easier to land at the entry level because mentorship is faster in person.
What does pay look like for entry-level data analysts?+
Pay varies by location, employer, industry, and experience level. Use this guide to understand what affects compensation and what skills can help you stand out.

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