1

Data Support Analyst Jobs in Georgia (NOW HIRING)

Within the first 90 days, complete Support's technical and functional onboarding program and ... Investigate application code, logs, and data flows; reproduce issues where possible; apply ...

Within the first 90 days, complete Support's technical and functional onboarding program and ... Investigate application code, logs, and data flows; reproduce issues where possible; apply ...

Within the first 90 days, complete Support's technical and functional onboarding program and ... Investigate application code, logs, and data flows; reproduce issues where possible; apply ...

Technical Support Analyst

Alpharetta, GA · On-site

$44K - $74K/yr

... Data Management. You can learn more about LexisNexis Risk at the link below, About the Team The ... About the Role The Technical Support Analyst is a customerfacing role focused on providing ...

Technical Support Analyst

Alpharetta, GA · On-site

$44K - $74K/yr

... Data Management. You can learn more about LexisNexis Risk at the link below, About the Team The ... About the Role The Technical Support Analyst is a customer-facing role focused on providing ...

Technical Support Analyst

Alpharetta, GA · On-site

$44K - $74K/yr

... Data Management. You can learn more about LexisNexis Risk at the link below, About the Team The ... About the Role The Technical Support Analyst is a customer-facing role focused on providing ...

Account Support Analyst

Alpharetta, GA · Hybrid

$36K - $61K/yr

The Account Support Analyst supports the organization in achieving revenue goals, ensuring customer ... Compiling data, verify pricing, compute fees and charges contract for billing purposes.

next page

Showing results 1-20

Data Support Analyst information

See Georgia salary details

$11

$31

$61

How much do data support analyst jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for data support analyst in Georgia is $31.32, according to ZipRecruiter salary data. Most workers in this role earn between $16.25 and $37.74 per hour, depending on experience, location, and employer.

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

To thrive as a Data Support Analyst, you need strong analytical skills, attention to detail, and a background in data management, often supported by a degree in information technology, computer science, or a related field. Familiarity with database systems (like SQL), data visualization tools (such as Tableau or Power BI), and ticketing systems is typically required. Excellent problem-solving abilities, communication skills, and customer service orientation help you stand out in this position. These skills and qualities are crucial for ensuring data accuracy, resolving user issues efficiently, and supporting data-driven decision-making within organizations.

Is 40 too late for data science?

A Data Support Analyst role involves working with data to support decision-making, and transitioning into data science at age 40 is possible with relevant skills such as programming, statistics, and tools like Python or R. Many professionals successfully switch careers later in life by gaining certifications or additional training, and age is generally not a barrier if you develop the necessary technical expertise and experience.

What is the difference between Data Support Analyst vs Data Analyst?

AspectData Support AnalystData Analyst
Required CredentialsBachelor's in IT, Computer Science, or related field; certifications like Microsoft Certified Data AnalystBachelor's in Statistics, Mathematics, or related; certifications like Microsoft Certified Data Analyst or Tableau
Work EnvironmentSupport teams, IT departments, data management systemsData-driven departments, business intelligence teams, analytics departments
Employer & Industry UsageIT firms, finance, healthcare, retailMarketing, finance, consulting, research

The Data Support Analyst primarily focuses on maintaining data systems, troubleshooting issues, and supporting data infrastructure. In contrast, the Data Analyst interprets data, creates reports, and provides insights for decision-making. While both roles require technical skills and data knowledge, their core responsibilities differ, with the Data Support Analyst emphasizing system support and the Data Analyst emphasizing data analysis and reporting.

Is a data analyst a high paid job?

Data analysts typically earn competitive salaries that vary by industry, experience, and location. Entry-level positions may start lower, but experienced analysts with skills in tools like Excel, SQL, and data visualization can earn higher wages, making it a well-compensated role in the data field.

What is a data support analyst?

A data support analyst is a professional who assists with managing, analyzing, and troubleshooting data systems and databases. They often work with data tools like SQL, Excel, and data visualization software to ensure data accuracy and support decision-making processes.

How does a Data Support Analyst typically collaborate with other departments within an organization?

Data Support Analysts frequently work with teams such as IT, business operations, and data engineering to troubleshoot data issues, clarify requirements, and ensure data accuracy. They often act as a bridge between technical staff and business users, translating data needs and troubleshooting requests. Effective communication and collaboration are essential, as you may be called upon to participate in cross-functional meetings, assist in data integration projects, and support end-users in understanding data reports or dashboards.

What jobs in the US pay 300,000 a year?

For a Data Support Analyst, earning $300,000 annually is uncommon, as this role typically has a median salary below that threshold. High-paying roles in data and technology, such as data scientists, data engineers, or senior analytics managers, are more likely to reach or exceed this level with extensive experience, advanced skills, and relevant certifications. These positions often require specialized expertise, leadership responsibilities, and sometimes work in high-demand industries or companies.
What are popular job titles related to Data Support Analyst jobs in Georgia? For Data Support Analyst jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Data Support Analyst jobs in Georgia look for? The top searched job categories for Data Support Analyst jobs in Georgia are:
What are popular job titles related to Data Support Analyst jobs in GA? For Data Support Analyst jobs in GA, the most frequently searched job titles are:
Infographic showing various Data Support Analyst job openings in Georgia as of June 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, 1% Temporary, and 1% Contract. Highlights an 82% Physical, 7% Hybrid, and 11% Remote job distribution, with an average salary of $65,148 per year, or $31.3 per hour.
Support Analyst

Full-time

Posted 2 days ago


Job description

This role sits at the center of PowerPlan’s customer experience, combining technical problemsolving, customer communication, and product expertise to help customers achieve their business goals. You will work handson with complex enterprise software, troubleshoot real customer issues, and grow into a trusted technical advisor. The role offers meaningful exposure to financial systems, scalable enterprise platforms, and emerging AIenabled tooling in a missioncritical environmnt.

COMPANY

For more than 30 years, PowerPlan has helped North American energy and utility organizations make confident financial decisions that improve lives and power the world. Our software is trusted by CFOs and finance leaders to manage complex assets, regulatory requirements, and financial processes at scale. We value collaboration, accountability, continuous learning, and a strong commitment to customer success.


KEY PERFORMANCE OBJECTIVES (First 12–18 Months)

1: Deliver Reliable, HighQuality Customer Case Resolution (First 6 Months)

Outcome:
Within the first 90 days, complete Support’s technical and functional onboarding program and independently resolve 80–85% of assigned customer support cases within defined SLAs while maintaining less than 5% overdue cases. By six months, consistently balance case volume, resolution speed, and documentation quality across a varied and growing support workload.

Impact:
Reliable case resolution protects customer trust, reduces escalations, and enables the support organization to scale efficiently while maintaining service quality.

How:
Triage and prioritize cases effectively, apply structured troubleshooting, document root causes and resolutions clearly, communicate status proactively, and escalate appropriately when complexity or risk requires it.

2: Resolve Complex Technical Issues Through Strong Root Cause Analysis (First 12 Months)

Outcome:
Within six months, independently analyze and resolve at least one complex customer issue per week requiring codelevel investigation (e.g., PowerBuilder, SQL, .NET, PL/SQL). By twelve months, handle highseverity technical issues endtoend with minimal reliance on senior escalation.

Impact:
Strong root cause analysis reduces repeat incidents, shortens time to resolution for critical issues, and improves overall product stability.

How:
Investigate application code, logs, and data flows; reproduce issues where possible; apply disciplined analytical techniques; collaborate with engineering when needed; and document findings for reuse.

3: Own Customer Communication and Satisfaction Throughout Issue Resolution (First 12 Months)

Outcome:
Consistently provide clear, proactive communication for all assigned cases, achieving an average customer satisfaction rating of 4.5 or higher. Over time, be recognized as a reliable point of contact who owns issues endtoend, including escalations.

Impact:
Strong communication reduces customer frustration, builds trust during complex resolutions, and strengthens longterm customer relationships.

How:
Set expectations early, provide regular updates, explain technical issues in customerappropriate language, coordinate across teams, and confirm satisfaction after resolution.

4: Build Product Expertise and Leverage Knowledge to Elevate Team Performance (First 12 Months)

Outcome:
Within nine months, develop deep expertise in one to two PowerPlan products or modules, enabling independent resolution of most modulespecific issues. By twelve months, contribute at least five reusable knowledge assets and actively support peers through mentoring and escalation assistance.

Impact:
Shared expertise improves consistency, reduces dependency on senior resources, and raises overall team capability.

How:
Study product architecture, review historical cases, document recurring solutions, participate in knowledgesharing forums, and support peers during complex investigations.

Leverage AI and Machine Learning Tools to Improve Support Effectiveness and Insight (First 12–18 Months)

Outcome:
Adopt and apply AIenabled tools and machine learning techniques to enhance support workflows, improve anomaly detection or diagnosis, and generate actionable insights from customer and system data.

Impact:
Thoughtful AI adoption improves efficiency, enables earlier detection of systemic issues, and supports scalable, highquality enterprise support.

How:
Apply appropriate ML techniques, explore NLP use cases for support data, collaborate with product or data teams, and evaluate outputs using sound validation and performance metrics.


  • Experience resolving customer support issues in enterprise software environments with strong SLA and documentation discipline
  • Solid technical foundation across databases, application architectures, and debugging tools (SQL, PowerBuilder, .NET, PL/SQL)
  • Strong written and verbal communication skills with a customerfirst mindset
  • Ability to analyze complex systems methodically and identify true root causes
  • Experience developing deep product or module expertise and sharing knowledge with peers
  • Preferred: Experience with AI or machine learning projects, including supervised or unsupervised learning techniques; familiarity with ML libraries such as scikitlearn, TensorFlow, PyTorch, or Keras; exposure to NLP techniques; and experience working with data pipelines, feature engineering, or model evaluation—especially in financial, anomaly detection, or enterprise software contexts

PowerPlan is an EOE

Applicant and Candidate Privacy Notice

Please note that this is a hybrid role that involves a combination of onsite work from our corporate office as well as work from home. While we strive to accommodate flexible working arrangements when sensible, there will be times when onsite work is required. This could include scheduled office days, team meetings, client meetings, or special events.

Â