Python is the second most requested skill in data analytics, and it signals a step up in technical complexity. These roles go beyond dashboard-building — they involve automation, statistical modeling, data cleaning at scale, and working with libraries like Pandas, NumPy, and Matplotlib.
$80,000 – $120,000/year
Python + SQL together command the strongest salary premiums in analytics.
Selby Jennings — New York, NY
New Seasons Market — Portland, Oregon
StorageMart — Columbia, Missouri
SimVentions — US-VA-Dahlgren
J.B. Hunt Transport Services, Inc. — Lowell, AR
Moon Active — Tel Aviv
Collēctīvus Holdings — Remote
Tential Solutions — Rockville, MD
Los Angeles Dodgers — Los Angeles, CA
BAE Systems, Inc. — Merrimack, New Hampshire, United States
Python-skilled data analysts typically earn 15–25% more than their Excel-only counterparts, translating to roughly $9,000–$15,000 in additional annual salary. The most requested Python libraries for analyst roles are Pandas (data manipulation), NumPy (numerical computing), Matplotlib and Seaborn (visualization), and SciPy (statistical analysis). Employers hiring for Python-focused analyst roles often expect you to automate repetitive tasks, build data pipelines, and perform more sophisticated analyses than what's possible in spreadsheets. Python skills are also a common stepping stone toward data science and analytics engineering roles. If you know both SQL and Python, you're in an excellent position — this combination appears in the majority of mid-to-senior analyst job descriptions.