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Python Data Analyst Jobs in Buffalo, NY (NOW HIRING)

Itero Group

Senior Snowflake Data Engineer

Boston, NY

$108K - $130K/yr

Develop dimensional data models, data marts, and curated analytical datasets. * Optimize Snowflake ... Strong Python development experience. * Experience building enterprise ELT/ETL pipelines.

New

Munich Re

Data Scientist, AI

Buffalo, NY · On-site +1

Explain advanced analytic and modeling procedures in the language that audiences with no predictive ... Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside ...

Wipro Limited

MSSQL Database Admin/Developer

Irving, NY · On-site

$60K - $135K/yr

Mandatory Skills - MS SQL, Python, Tableau, QlikView Overview Highly skilled and experienced Lead Developer with a strong focus on data analytics. The ideal candidate will be responsible for the ...

New

Praxair

Sr. Data Engineer

Tonawanda, NY · On-site

$108K - $158K/yr

Partner with data scientists, analysts, architects, and IT teams to define requirements, deliver ... Advanced proficiency in SQL, Python, and/or Scala. * Handson expertise with Microsoft Azure ...

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All JobsPython Data Analyst JobsPython Data Analyst Jobs in Buffalo, NY

Python Data Analyst information

See Buffalo, NY salary details

$32.9K

$80.1K

$131.7K

How much do python data analyst jobs pay per year?

As of Jul 18, 2026, the average yearly pay for python data analyst in Buffalo, NY is $80,051.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What does a Python Data Analyst do?

A Python Data Analyst leverages the Python programming language to collect, process, and analyze large sets of data. They use tools and libraries like Pandas, NumPy, and Matplotlib to clean data, perform statistical analysis, and create visualizations that help organizations make data-driven decisions. Their role often involves extracting insights from complex datasets, automating data workflows, and communicating findings to stakeholders through reports or dashboards. Python Data Analysts play a crucial part in turning raw data into actionable business intelligence.

How do Python Data Analysts typically collaborate with other departments within an organization?

Python Data Analysts often work closely with teams such as marketing, finance, and product development to provide data-driven insights that inform business decisions. They regularly participate in cross-functional meetings to understand departmental objectives, gather requirements for data analysis, and present their findings in an accessible manner. Effective communication and the ability to translate technical results into actionable recommendations are essential, as analysts often act as a bridge between technical data and non-technical stakeholders.

What is the difference between Python Data Analyst vs Data Scientist?

AspectPython Data AnalystData Scientist
Required SkillsPython, SQL, data visualization, statistical analysisPython, R, machine learning, statistical modeling
Work EnvironmentBusiness analytics, reporting, data cleaningAdvanced modeling, predictive analytics, research
Industry UsageFinance, marketing, healthcare, retailTech, finance, research, AI development

While both roles require Python and data analysis skills, Data Scientists typically engage in more complex modeling and machine learning, whereas Python Data Analysts focus on data cleaning, visualization, and reporting to support business decisions.

What Does a Python Data Analyst Do?

As a Python data analyst, you use the Python programming language to develop tools for data mining, analysis, and data visualization. You typically develop a script to meet the specific data needs of your client or employer. Then, you test your code and perform debugging duties before deploying it in a live environment. Some data analysts also have algorithm creation responsibilities. In this case, after creating and testing an algorithm, you use Python with your algorithm to interpret data. You also develop reports to show to your clients or employers, and you may code a web app or interface that clients can use to visualize data sets.

Will AI replace a data analyst?

AI tools can automate routine data processing and analysis tasks, but the role of a data analyst involves interpreting insights, understanding business context, and communicating findings, which require human judgment. Data analysts who develop skills in programming, data visualization, and machine learning can adapt to new technologies and continue to add value in data-driven decision-making.

Is 40 too old to become a data analyst?

Age is not a barrier to becoming a data analyst; many professionals transition into the field later in life. Success depends on acquiring relevant skills such as SQL, Python, and data visualization, along with practical experience and certifications. Employers value diverse backgrounds and experience, making it possible to start a data analyst career at any age.

What are the key skills and qualifications needed to thrive as a Python Data Analyst, and why are they important?

To thrive as a Python Data Analyst, you need strong analytical skills, a solid grasp of statistics, and proficiency in Python programming, often supported by a degree in data science, mathematics, or a related field. Familiarity with data analysis libraries like pandas and NumPy, visualization tools such as Matplotlib or Seaborn, and experience with data querying languages like SQL are typically required. Attention to detail, critical thinking, and effective communication help you derive insights and present findings clearly to stakeholders. These skills and qualities are vital for transforming raw data into actionable business intelligence and supporting data-driven decision-making.

Is Python a high paying job?

Python Data Analysts are generally well-compensated due to their technical skills in programming, data manipulation, and analysis. Salaries vary based on experience, location, and industry, but proficiency in Python often leads to higher earning potential compared to many other entry-level roles in data analysis. Certifications and knowledge of related tools like SQL or machine learning can further increase salary prospects.

Is Python useful for data analysts?

Python is highly useful for data analysts because it offers powerful libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization. It is widely used in the industry for automating tasks, building data pipelines, and performing statistical analysis, making it a valuable skill for the role.
More about Python Data Analyst jobs
What are the most commonly searched types of Python Data Analyst jobs in Buffalo, NY? The most popular types of Python Data Analyst jobs in Buffalo, NY are:
What are popular job titles related to Python Data Analyst jobs in Buffalo, NY? For Python Data Analyst jobs in Buffalo, NY, the most frequently searched job titles are:
What job categories do people searching Python Data Analyst jobs in Buffalo, NY look for? The top searched job categories for Python Data Analyst jobs in Buffalo, NY are:
What cities near Buffalo, NY are hiring for Python Data Analyst jobs? Cities near Buffalo, NY with the most Python Data Analyst job openings:
Python Data Analyst jobs near you
Infographic showing various Python Data Analyst job openings in Buffalo, NY as of July 2026, with employment types broken down into 1% Locum Tenens, 1% Internship, 81% Full Time, 11% Part Time, 1% Temporary, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $80,051 per year, or $38.5 per hour.
Senior Snowflake Data Engineer

Senior Snowflake Data Engineer

Itero Group

Boston, NY

$108K - $130K/yr

Other

Posted 4 days ago

New

Job description

 

Itero Group is seeking an experienced Senior Snowflake Data Engineer to support enterprise data modernization initiatives for leading Financial Services clients. This individual will design, develop, and optimize cloud-based data platforms using Snowflake while partnering with business stakeholders, architects, and analytics teams to deliver scalable, secure, and high-performing data solutions.

The ideal candidate has deep experience building enterprise data pipelines, implementing cloud data warehouses, and supporting regulatory and financial reporting initiatives within banking, insurance, capital markets, or asset management organizations.

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines using Snowflake.
  • Build and optimize ELT/ETL processes to ingest data from multiple enterprise systems.
  • Develop dimensional data models, data marts, and curated analytical datasets.
  • Optimize Snowflake performance through clustering, partitioning, workload management, and cost optimization.
  • Implement data quality, lineage, governance, and security best practices.
  • Develop solutions utilizing Snowpark, stored procedures, streams, tasks, and dynamic tables where appropriate.
  • Partner with business stakeholders to understand reporting and analytical requirements.
  • Build reusable data frameworks supporting Finance, Risk, Compliance, Treasury, and Regulatory Reporting.
  • Develop automated orchestration using Airflow, Azure Data Factory, AWS Glue, or similar technologies.
  • Participate in Agile ceremonies including sprint planning, backlog grooming, and code reviews.
  • Mentor junior engineers and promote engineering best practices.
Required Qualifications
  • 7+ years of Data Engineering experience.
  • 4+ years of hands-on Snowflake development.
  • Strong SQL expertise including query optimization.
  • Strong Python development experience.
  • Experience building enterprise ELT/ETL pipelines.
  • Experience with cloud platforms:
    • Azure
    • AWS
    • GCP (preferred)
  • Experience with:
    • dbt
    • Airflow
    • Git
    • CI/CD
    • Terraform (preferred)
  • Experience working with structured and semi-structured data.
  • Knowledge of data modeling (Star Schema, Snowflake Schema, Kimball).
  • Experience with REST APIs and data integration.
  • Strong troubleshooting and performance tuning skills
Financial Services Experience Required

Candidates should possess experience supporting one or more of the following:

  • Banking
  • Commercial Banking
  • Investment Banking
  • Capital Markets
  • Asset Management
  • Wealth Management
  • Insurance
  • FinTech
Consulting Experience
  • Experience working in consulting environments such as PwC, Deloitte, EY, KPMG, Accenture, Slalom, or similar.
  • Ability to gather requirements directly from business stakeholders.
  • Experience leading client workshops and solution design sessions.
  • Excellent verbal and written communication skills.
  • Strong presentation and stakeholder management abilities.
 

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