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.
206 jobs found
Data Analyst
State of Idaho (Office of Information Technology Services) — Boise, Idaho
Principal Data Analyst (Member Financial Solutions Strategy & Analytics)
Navy Federal — Pensacola, Florida, United States
Data Analyst- Psychometrics
American Institute of Certified Public Accountants — Not specified
Data Analyst Senior - Space Manager
Tulk Llc — Springfield, Virginia, United States
Data Analyst Mid - GEOINT Technical SME
Tulk Llc — St. Louis, Missouri, United States
Data Analyst
SENKO Advanced Components — Camarillo, California, United States
Consultant, Audit Data Analyst
Coca-Cola — Atlanta, Georgia, United States
Business Analyst, Customer Delivery Excellence
Amazon — Bellevue, Washington, United States
IT Business Analyst
Baird — Milwaukee, Wisconsin, United States
Data Analyst
OneGlobe — Washington, District of Columbia, United States
What You Need to Know
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.