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Freelance Remote Big Data Jobs (NOW HIRING)

Cloudacio

Data Engineer (Contract) (Remote)

Manhattan, NY · Remote

$126K - $151K/yr

Embracing a remote and flexible work philosophy, we offer our team the freedom to collaborate and ... Work with big data technologies to solve complex data processing challenges. * Implement ETL ...

Elder Research

Data Analyst (Remote)

Prepares big data, implements data models developed by others, and provides database support for ... Remote Work: in an industry of declining remote work opportunities. * People-Focused Culture: we ...

Elder Research

Data Analyst (Remote)

Prepares big data, implements data models developed by others, and provides database support for ... Remote Work: in an industry of declining remote work opportunities. * People-Focused Culture: we ...

Ruri Software Technologies LLC

GCP Data Engineer

$117K - $140K/yr

Remote Skills:- * Experience working in GCP based Big Data deployments (Batch/Real-Time)Leveraging Big Query,Fusion, Dataflow, Dataproc, etc. * Demonstrated mastery in Google BigQuery * Strong ...

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How much do freelance remote big data jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for freelance remote big data in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.
What are the most commonly searched types of Remote Big Data jobs? The most popular types of Remote Big Data jobs are:
Freelance Remote Big Data jobs near you
Infographic showing various Freelance Remote Big Data job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.

Data Scientist (AI, Big Data, SQL, Python) - W2 Only - REMOTE

Resource Point LLC

Minneapolis, MN • Remote

Contractor

Re-posted 17 days ago

Job description

Job Title: Data Scientist (AI, Big Data, SQL, Python) - W2 Only - REMOTE

Location: Minneapolis, MN

Duration: 12+ Months

Description:
Our audit and governance functions require a centralized data leader who can:

  • Architect scalable, secure, compliant data pipelines
  • Translate complex datasets into actionable insights for regulatory and operational decisions
  • Build intuitive, low-maintenance tools that empower non-technical users across the PA experience

Responsibilities:

  • Data Collection & Cleaning - They gather data from various sources and clean it to ensure it's usable—removing errors, filling in missing values, and standardizing formats.
  • Exploratory Data Analysis (EDA) - They explore the data to understand patterns, trends, and relationships using statistical techniques and visualizations.
  • Model Building - They build predictive models using machine learning algorithms to forecast outcomes or classify data.
  • Interpretation & Communication - They translate complex results into actionable insights and communicate them to stakeholders through reports, dashboards, or presentations.
  • Deployment & Monitoring - In some cases, they help deploy models into production systems and monitor their performance over time.

Ideal Background: 

  • Healthcare specific background would be helpful.
  • But candidate must be experienced in elements of statistics, computer science, and domain expertise to help organizations make data-driven decisions.
  • As well as, build and maintain artificial intelligence (AI) driven platforms/solutions.

Required Skills:

  • Programming: Python, R, SQL
  • Statistics & Mathematics
  • Machine Learning & AI
  • Data Visualization: Tools like Tableau, Power BI, or libraries like Matplotlib and Seaborn
  • Big Data Tools: Spark, Hadoop (for large-scale data)

Preferred:

  • Advanced SQL and Python for analytics, ETL, and automation
  • Data modeling, warehousing, and pipeline orchestration (cloud, native stack)
  • Dashboarding (Power BI; Streamlit or similar) and reproducible analytics (versioning, CI/CD preferred)
  • Healthcare data familiarity (claims, PA & appeals, pharmacy) and regulatory contexts (CMS, NCQA, URAC, ERISA, state rules)
  • Data security, privacy, and compliance best practices.