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Data Analytics Manager Jobs in Inman, SC (NOW HIRING)

Pwc

Data Strategy-Manager

Spartanburg, SC · On-site

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract ...

Pwc

Data Governance- Manager

Spartanburg, SC · On-site

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract ...

Regional Finance

Senior Associate, Data Analyst

Greer, SC · On-site +1

$82K - $104K/yr

Develop proposals for fully automated data and reporting solutions and present to management/stakeholders * Support data ownership and governance activities for the Data & Analytics team, including ...

Posted today

Staffingine LLC

Data Analyst

Greenville, SC · On-site

Job Title: Data Analyst Job Location: Greenville, SC Job Type: Contract * Help prepare borrowing ... Coordinating and preparing necessary material for investor presentations, management reporting, and ...

AFL

Data Governance Analyst

Duncan, SC · On-site

Bachelor's degree in Data Analytics, Computer Science, Information Management, Mathematics, Engineering or a related discipline * 3-5+ years of experience in business analysis, data analysis, or data ...

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All JobsData Analytics JobsData Analytics Manager JobsData Analytics Manager Jobs in Inman, SC

Data Analytics Manager information

See Inman, SC salary details

$26.4K

$82.6K

$146.3K

How much do data analytics manager jobs pay per year?

As of Aug 12, 2026, the average yearly pay for data analytics manager in Inman, SC is $82,635.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,100.00 and $106,800.00 per year, depending on experience, location, and employer.

How do data analytics managers typically collaborate with stakeholders from non-technical departments?

Data Analytics Managers often act as a bridge between technical data teams and non-technical stakeholders, such as marketing, finance, or operations. They translate complex data insights into actionable recommendations and ensure that analyses align with business objectives. Regular communication, tailored presentations, and workshops are common practices to ensure all stakeholders understand the value and limitations of analytical findings. This collaborative approach helps drive data-driven decision-making across the organization.

What are the key skills and qualifications needed to thrive as a data analytics manager?

To thrive as a Data Analytics Manager, you need strong analytical skills, expertise in statistical methods, and a background in data science or a related field, often supported by a bachelor's or master's degree. Proficiency with data visualization tools (such as Tableau or Power BI), SQL, and analytics platforms like Python or R is typically required, along with experience in managing data projects. Leadership, strategic thinking, and effective communication are important soft skills for leading teams and translating data insights into actionable business strategies. These skills ensure that analytical initiatives drive business value and support informed decision-making across the organization.

What does a data analytics manager do?

A Data Analytics Manager oversees data analysis operations and leads a team of analysts to extract actionable insights from data. They are responsible for managing data-driven projects, ensuring data integrity, and presenting findings to help guide business decisions. Their role often involves collaborating with various departments, setting analytic strategies, and ensuring that the team uses the most effective tools and methodologies. Additionally, they may handle hiring, training, and performance reviews of analytics staff.

What is the difference between Data Analytics Manager vs Data Analyst?

AspectData Analytics ManagerData Analyst
ResponsibilitiesOversees analytics projects, manages teams, develops strategiesPerforms data collection, cleaning, and analysis to generate reports
Required SkillsLeadership, project management, advanced analyticsData manipulation, statistical analysis, visualization
QualificationsBachelor's or Master's in Data Science, Analytics, or related fields; certifications like CAP or Microsoft Certified Data AnalystBachelor's in Statistics, Mathematics, or related fields; certifications like Microsoft Certified Data Analyst
Work EnvironmentCorporate offices, analytics teams, cross-department collaborationData teams, business units, often in office or remote settings

In summary, a Data Analytics Manager leads analytics teams and strategies, requiring leadership skills and advanced certifications, while a Data Analyst focuses on data processing and reporting, with more technical and analytical tasks. Both roles are essential in data-driven organizations and often work closely together.

What cities near Inman, SC are hiring for Data Analytics Manager jobs? Cities near Inman, SC with the most Data Analytics Manager job openings:
Data Analytics Manager jobs near you
Infographic showing various Data Analytics Manager job openings in Inman, SC as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $82,635 per year, or $39.7 per hour.

Data & Analytics Developer

West Coast Logistics

Greenville, SC

$47 - $53/hr

Contractor

Posted 11 days ago

Job description

CLIENT HIGHLIGHT
The client you'll be supporting is a Fortune 500 global leader in energy technology, focused on helping the world produce cleaner, more reliable power. Their teams design and improve the systems that keep homes, businesses, and communities running, from gas and wind turbines to the electrical grids that connect them. This is a chance to be part of a company that's driving innovation, supporting sustainability, and shaping the future of energy.

LOCATION
Greenville, South Carolina, 29615

COMPENSATION
$47-53 per hour, full benefits offered

SCHEDULE
Hybrid; local candidates
Standard Hours: 40 hours per week

CONTRACT TERM
1 year with high likelihood of extension or conversion

POSITION OVERVIEW
The Data & Analytics (D&A) Developer II / Data Scientist supports the HDPE Operations & Strategy team, serving as the bridge between engineering domain knowledge, business operations, and IT execution. This role defines data requirements, builds AI/ML and scenario-planning models, and delivers harmonized insights and reporting to business stakeholders worldwide. Responsibilities: Analyze data across enterprise systems (SAP, Salesforce, Databricks, Power BI); develop and validate machine learning models for forecasting and scenario planning; build and maintain Python-based data pipelines; track project execution through P6 and other project systems; reverse-engineer existing dashboards and SQL logic; and translate technical findings into actionable business insights.

RESPONSIBILITIES
Data Analysis & Intelligence
  • Analyze data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor and finance systems) to identify patterns, gaps, and improvement opportunities
  • Work with Program Managers and Operations leaders to define relevant data assets and specify how data should be accessed, interpreted, and used
  • Transform large structured/unstructured datasets (100k+ rows) into actionable insights
  • Conduct data quality checks and resolve data defects across enterprise platforms
AI/ML Model Development & Deployment
  • Develop and validate machine learning models supporting demand forecasting and scenario modeling
  • Document analytical findings, model performance, and data definitions for transparency and reproducibility
  • Build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows in collaboration with Data Engineers
  • Translate business data challenges into concrete data science and AI/ML problem statements
  • Leverage LLMs and prompt engineering to build tools that augment decision-making and automate workflows
Scenario Planning & Project Execution Analytics
  • Design and execute scenario planning models to test business assumptions and evaluate what-if outcomes
  • Track project execution data across P6 (Primavera) and other systems, linking planning assumptions to actual performance
  • Support variance analysis between planned assumptions and actual execution to identify gaps and trends
  • Build automated tracking solutions monitoring assumption validity through project lifecycle stages
  • Collaborate with Program Managers to refine planning assumptions based on execution learnings
  • Provide data pipelines and data to build executive dashboards visualizing assumption-to-execution alignment
Existing Data Ecosystem & Optimization
  • Review existing dashboards, models, and data pipelines to understand design patterns and data flows
  • Read and interpret SQL queries and business logic embedded in current reports and analytical systems
  • Identify opportunities to optimize or consolidate existing reporting and modeling assets
  • Maintain consistency with established data standards and best practices
Business Stakeholder Collaboration & Continuous Improvement
  • Translate complex data findings and model outputs into clear, actionable insights for technical and non-technical audiences
  • Support centralized, KPI-based reporting solutions for business stakeholders across global business lines
  • Collaborate with Data Analysts and Data Engineers to ensure data requirements are implemented correctly at the pipeline and infrastructure level
  • Stay current with AI, ML, and data science advancements, proposing new approaches to enhance solutions
REQUIREMENTS
  • Bachelor's degree in Data Science, Computer Science, Engineering, or related field.
  • Strong proficiency in Python (pandas, numpy, scikit-learn, scipy) and SQL.
  • Experience with statistical modeling, scenario/what-if analysis, and model validation.
  • Familiarity with enterprise data systems (SAP, Salesforce, Databricks) and LLM/prompt engineering concepts.
  • Strong communication skills and ability to work across international, multicultural teams.