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.
309 jobs found
Business Intelligence Analyst
Milwaukee Bucks Inc. — Milwaukee, WI, US
Staff Marketing Data Analyst
Credit Genie — New York, NY; Philadelphia, PA; Plymouth Meeting, PA; San Francisco, CA; Toronto, ON
Analyst II, Casino Analytics
DraftKings Inc. — Boston, MA
Category Analyst
Love's Travel Stops — Oklahoma City, OK
Senior Data Analyst (MedTech)
Yipitdata — Remote, United States
Big Data Analyst (TS/SCI)
Vantor — Herndon, Virginia, United States
Senior Data Analyst
Wal-Mart Transportation — Bentonville, Arkansas, United States
Senior, Data Analyst
Sam's West — Bentonville, Arkansas, United States
Pricing Analyst III
Thermo Fisher Scientific — Pittsburgh, Pennsylvania
Sr Data Analyst
Aflac — Remote
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.