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Python Data Analyst Jobs in Columbia, SC (NOW HIRING)

... data collection cleaning and feature engineering to prepare datasets for modeling ... Develop predictive models and statistical analyses using Python R or similar tools * Deploy monitor ...

... data collection cleaning and feature engineering to prepare datasets for modeling ... Develop predictive models and statistical analyses using Python R or similar tools * Deploy monitor ...

Lead Analytics Engineer

Columbia, SC · Remote

$80K - $115K/yr

Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. • Advanced proficiency in Python for data manipulation and analytics engineering ...

... Python, Shell Extremely comfortable developing on Linux servers Some experience of web development stacks, analytics, NoSQL data stores, data modeling, analytical tools and libraries Solid ...

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Python Data Analyst information

See Columbia, SC salary details

$31.5K

$76.5K

$125.8K

How much do python data analyst jobs pay per year?

As of Jul 13, 2026, the average yearly pay for python data analyst in Columbia, SC is $76,453.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,800.00 and $89,700.00 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.
What are the most commonly searched types of Python Data Analyst jobs in Columbia, SC? The most popular types of Python Data Analyst jobs in Columbia, SC are:
What job categories do people searching Python Data Analyst jobs in Columbia, SC look for? The top searched job categories for Python Data Analyst jobs in Columbia, SC are:
Infographic showing various Python Data Analyst job openings in Columbia, SC 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 81% Physical, 5% Hybrid, and 14% Remote job distribution, with an average salary of $76,453 per year, or $36.8 per hour.
Lead Business Intelligence Developer -- Founding Team

Lead Business Intelligence Developer -- Founding Team

Annuity Health

Columbia, SC • On-site

$120K - $225K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted yesterday


Annuity Health rating

6.4

Company rating: 6.4 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

312th of 449 rated business services


Job description

Description:

The short version

Healthcare revenue cycle management runs on numbers — AR days, denial rates, collection yield — and most of the industry still assembles them by hand, in spreadsheets, a month late. We’re a profitable, established RCM company building our technology platform from line zero, and analytics is a first-class part of it, not an afterthought bolted on later. You’d be the founding BI hire: the person who decides how this company defines, models, and presents its numbers.

Your work has two audiences from day one. Our own revenue cycle teams, who will run their operation on what you build. And the healthcare providers we serve, who will judge us partly on the clarity of the reporting we put in front of them.


Why this role exists

Most BI roles inherit a semantic layer someone else modeled, metric definitions nobody agrees on, and a backlog of dashboard requests. This one inherits nothing — there’s a modern lakehouse going in (Databricks and Microsoft Fabric) and a founding engineering team building alongside you. You’ll own everything north of the gold layer: the semantic models, the metric definitions, and every report a human sees. How far you reach into the gold layer itself depends on you — at minimum you’ll shape it as its most important customer; if you have the data engineering chops to own parts of it, that scope is yours to take.


What you’ll do

  • Own the semantic layer end to end — Power BI models on Databricks and Fabric, built for performance, maintainability, and trust
  • Define the company’s metrics: one shared definition of AR days, denial rate, net collection rate, and the rest, so every team and every client sees the same truth
  • Build the provider-facing reporting our clients rely on — visually engaging, intuitive, fast, and credible enough to anchor a business review. Information design is part of the craft here.
  • Build the internal analytics our revenue cycle teams use to run their daily operation and to measure whether our automation is actually working
  • Establish our first data-analyst agents — automated analysis that monitors the data continuously, surfaces trends and anomalies, and drafts the first pass of insight before anyone thinks to ask.
  • Shape the gold layer as its primary consumer — and take ownership of transformations if that’s in your toolkit
  • Set the BI standards, patterns, and review practices the function scales on as we grow
  • Sit with users on both sides — ops teams and client-facing leaders — and work problems, not ticket queues

How we work

This team is being built AI-native from day one:

  • We work problems, not tickets. You’ll sit close to the people who use your work, watch how they actually operate, and reason from first principles about what to build.
  • AI tools are part of the craft. You should already be using AI daily to write DAX, SQL, and documentation faster — and be excited to help define what an AI data analyst looks like here.
  • You own what ships. Numbers that reach a client or drive an operational decision are correct, tested, and explainable. Speed matters; trust matters more.

The stack: Power BI on Databricks and Microsoft Fabric, with SQL throughout and Python where it helps. Azure underneath.


Who you are

Must-haves:

  • 8+ years in BI/analytics with deep Power BI expertise — DAX, semantic modeling
  • Healthcare claims and payer data experience — you’ve worked with 835/837 transactions or adjacent claims data
  • Strong SQL and real experience working against a lakehouse or modern warehouse (Databricks, Fabric, Snowflake, or similar)
  • A track record of building reporting that external clients or executives actually relied on — not just internal dashboards

Nice-to-haves:

  • Revenue cycle operations depth — denial management, AR follow-up, payer behavior. You know what a CARC code is without looking it up
  • Gold-layer or transformation ownership (PySpark, dbt, Delta Live Tables, or Fabric dataflows)
  • Fabric-specific depth — Direct Lake, OneLake, deployment pipelines
  • Experience standing up a BI function or being the first analytics hire somewhere

Compensation & benefits

Base salary of $120,000–$225,000 — the range is wide because the scope is flexible: we’ll hire at lead or principal level depending on how much of the stack you can own, plus bonus. Annuity Health offers its employees excellent benefits including: Health, Dental, Vision, HSA and FSA Accounts, Voluntary Insurance, Paid Holidays, PTO, and 401(k).


How to apply

Apply with a link to your work or a few sentences about a reporting product you built that people genuinely relied on — what it measured, who used it, and why they trusted it.


Requirements:



What Annuity Health employees say

Pay

Hours and flexibility

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