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Python Data Analyst Jobs in Connecticut (NOW HIRING)

Whiz Global LLC

Data Warehouse Analyst

New Milford, CT · On-site

- Data Warehouse Analyst (SQL, Informatica) Location: Uncasville, CT (Hybrid) Job Type: Full-Time ... Knowledge of Python or Shell scripting. * Experience in regulated industries such as Healthcare, ...

Esprit Nexus Inc.

Be Seen First

Business Analyst IT

Stamford, CT · On-site

$35 - $55/hr

The ideal candidate will have strong data analysis skills, the ability to validate and troubleshoot ... Basic understanding of programming concepts (Ruby, Python, or Java preferred). * Excellent ...

Esprit Nexus Inc.

Be Seen First

Business Analyst IT

Stamford, CT · On-site

$35 - $55/hr

The ideal candidate will have strong data analysis skills, the ability to validate and troubleshoot ... Basic understanding of programming concepts (Ruby, Python, or Java preferred). * Excellent ...

BestEx Research

Execution Analyst

Stamford, CT · On-site

Proven programming, statistics, and data analysis skills (e.g., Python, SQL, R) * Strong analytical thinking and a demonstrated interest in solving complex problems * Excellent written and verbal ...

Bestex Research

Execution Analyst

Stamford, CT · On-site

Proven programming, statistics, and data analysis skills (e.g., Python, SQL, R) * Strong analytical thinking and a demonstrated interest in solving complex problems * Excellent written and verbal ...

Budderfly Inc

Reporting Analyst

Shelton, CT · Hybrid

Reporting Analyst: (Shelton, CT) Perform statistical data analysis & build forecasting & optimization models. Code, test rpts & dashboards using AWS Quicksight. Build data pipelines using Python.

Point72

Business Intelligence Analyst

Stamford, CT · On-site

Experience using Python for data profiling and analysis (e.g., Pandas, NumPy). * Hands-on experience with AI/LLM platforms and tooling, and familiarity applying foundation models, prompt engineering ...

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All JobsPython Data Analyst JobsPython Data Analyst Jobs in Connecticut

Python Data Analyst information

See Connecticut salary details

$32.3K

$78.6K

$129.4K

How much do python data analyst jobs pay per year?

As of Jul 9, 2026, the average yearly pay for python data analyst in Connecticut is $78,614.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $92,300.00 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
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Python Data Analyst jobs near you
Infographic showing various Python Data Analyst job openings in Connecticut as of July 2026, with employment types broken down into 33% Full Time, 33% Temporary, and 34% Contract. Highlights an 100% In-person job distribution, with an average salary of $78,614 per year, or $37.8 per hour.

Product Data Operations Analyst

NEFCO Construction Supply LLC

East Hartford, CT • On-site

$65K - $75K/yr

Other

Posted 8 days ago

Job description

Description

NEFCO is scaling from $1B to $5B, and high-quality product data is foundational to that growth. We are looking for a detail-oriented, technically curious Product Data Operations Analyst to maintain, enrich, and improve product data quality across ERP, PIM, supplier, and analytics systems - while identifying opportunities to automate workflows and strengthen data governance.


What You'll Do 

  • Maintain and enrich product data attributes - descriptions, specs, classifications, images, and identifiers (UNSPSC, GTIN, MPN) - across ERP and PIM systems.
  • Source missing attributes from suppliers, manufacturers, and third-party databases.
  • Support new product onboarding: review, validate, and enrich data before loading into downstream systems.
  • Apply data quality standards and run regular audits to catch gaps, inconsistencies, and duplicates.
  • Build and maintain dashboards tracking data quality KPIs, completeness scores, and enrichment progress.
  • Use SQL, Excel, Power Query, ETL tools, and AI-assisted scripting to automate data prep and validation workflows.
  • Partner with Pricing, Sales, Supply Chain, IT, and Operations to translate business needs into data requirements.
  • Support ERP/PIM improvement initiatives; assist with supplier portals, catalog syndication, and compliance projects.
  • Train end-users on data best practices and self-service tools.




Requirements

Qualifications

  •  Bachelor's degree in business, Information Systems, Data Analytics, or related field (or equivalent experience).
  • 2-4 years in product data, master data, PIM/ERP data management, catalog operations, or a related analytics role.
  • Solid SQL skills and strong Excel proficiency (pivot tables, Power Query, lookups, data validation).
  • Working knowledge of ETL concepts and comfort with scripting, automation, or AI-assisted data tools.
  • Experience with ERP or PIM systems; Epicor Eclipse or Salsify a plus.
  • Detail-oriented, organized, and able to manage competing priorities.
  • Clear communicator with both technical and non-technical stakeholders.
  • Power BI, Tableau, or similar BI tools.
  • Python for data manipulation and automation.
  • Experience with ETL pipelines, APIs, or FTP/SFTP data exchanges.
  • Background in industrial distribution, manufacturing, or wholesale.
  • Exposure to AI-assisted enrichment tools or LLMs for classification and content generation.
  • Familiarity with data governance, taxonomy management, or product classification.


What Success Looks Like

  • Product data is more complete, accurate, and easier for teams to use.
  • Data quality issues are discovered early and resolved through repeatable processes.
  • Manual cleanup is accomplished through automation, ETL workflows, and better source collection.
  • Product onboarding is consistent and less reliant on tribal knowledge.
  • Stakeholders trust the data because standards and ownership are clearly defined.

Apply