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Data Enablement Jobs in Georgia (NOW HIRING)

PTR Global

Data Analyst - Senior

Atlanta, GA · On-site

$58 - $62/hr

This role will serve as a key data enablement resource for AI agent development, reporting initiatives, workflow automation, and transformation efforts. * The successful candidate will source data ...

New

Coreforce

Senior Data Architect

Atlanta, GA · Hybrid

$64.75 - $86.50/hr

AI-First Data Enablement * Apply an AI-first mindset to data architecture by designing data structures, metadata, retrieval patterns, and governance models that support machine learning, generative ...

Utility Associates, Inc.

Be Seen First

Senior Data Architect

Atlanta, GA · On-site

$150K - $160K/yr

AI-First Data Enablement · Apply an AI-first mindset to data architecture by designing data structures, metadata, retrieval patterns, and governance models that support machine learning, generative ...

Coreforce

Senior Data Architect

Atlanta, GA · On-site

$150K - $160K/yr

AI-First Data Enablement * Apply an AI-first mindset to data architecture by designing data structures, metadata, retrieval patterns, and governance models that support machine learning, generative ...

Wolters Kluwer

Sales Enablement Manager

Kennesaw, GA

... data to shorten time-to-productivity as the GTM motion matures. Playbooks, Sales Plays & GTM ... Design an enablement framework built for change: as product offerings evolve from point solutions ...

Wolters Kluwer

Sales Enablement Manager

Kennesaw, GA

... data to shorten time-to-productivity as the GTM motion matures. Playbooks, Sales Plays & GTM ... Design an enablement framework built for change: as product offerings evolve from point solutions ...

Crowe LLP

Technology Enablement Manager

Atlanta, GA · On-site

Embed awareness of technology risk, data protection, AI governance, and ethical use into enablement activities in partnership with subject matter experts. Advise and Communicate: Produce strategic ...

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Data Enablement information

What is data enablement?

Data enablement is the process of making data accessible, usable, and valuable across an organization. It involves implementing tools, processes, and strategies that empower employees to access, analyze, and act on data efficiently. The goal of data enablement is to break down data silos, improve data literacy, and support better decision-making by ensuring the right people have the right data at the right time.

How does a data enablement professional typically collaborate with other departments to drive data-driven decision-making?

Data Enablement professionals work closely with departments such as marketing, finance, operations, and IT to ensure that accurate, accessible data informs strategic decisions. They often facilitate data literacy training, help teams define data requirements, and establish efficient data pipelines. Collaboration is key—regular meetings, workshops, and cross-functional projects are common to align data initiatives with business goals. This role also involves translating complex data concepts into actionable insights for non-technical stakeholders, fostering a culture of data-driven decision-making throughout the organization.

What are the key skills and qualifications needed to thrive in data enablement, and why are they important?

To thrive in Data Enablement, you need strong analytical skills, a solid understanding of data management principles, and experience with data governance, often supported by a degree in data science, information technology, or a related field. Familiarity with data visualization tools (such as Tableau or Power BI), data integration platforms, and database systems are commonly required, along with certifications like CDMP (Certified Data Management Professional). Excellent communication, collaboration, and problem-solving skills help you translate data insights into actionable business strategies and foster data literacy across teams. These competencies are crucial for ensuring high-quality, accessible data that drives informed decision-making and organizational growth.

What is the difference between Data Enablement vs Data Analyst?

AspectData EnablementData Analyst
Primary FocusProviding tools, platforms, and infrastructure to empower data usersAnalyzing data to generate insights and reports
Skills & CertificationsData management, platform administration, data governanceStatistical analysis, SQL, data visualization tools
Work EnvironmentIT teams, data platforms, cross-functional teamsBusiness units, analytics teams, reporting environments
Employer & Industry UsageTech companies, large enterprises, data-driven organizationsMarketing, finance, operations departments across industries

Data Enablement focuses on building and maintaining the infrastructure and tools that allow organizations to access and utilize data effectively. In contrast, Data Analysts interpret and analyze data to provide actionable insights. While both roles work with data, Data Enablement is more technical and infrastructure-oriented, whereas Data Analysts are more focused on analysis and reporting.

What are popular job titles related to Data Enablement jobs in Georgia?

For Data Enablement jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Data Enablement jobs in Georgia look for?

The top searched job categories for Data Enablement jobs in Georgia are:

Data Enablement jobs near you
Infographic showing various Data Enablement job openings in Georgia as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 11% Part Time, 7% Contract, and 3% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Data Engineer (AI & Data Enablement)

Innovatix Technology Partners

Atlanta, GA • On-site

$110K - $132K/yr

Other

Posted 2 days ago

New

Job description

We are seeking a Senior Enterprise Data Engineer – AI & Data Enablement to support enterprise-scale data sourcing, engineering, quality, and enablement initiatives. The ideal candidate will come from a strong Data Engineering background and have recently expanded into AI/ML or GenAI projects.

This role is primarily focused on enterprise data engineering and sourcing, ensuring high-quality, reliable, and accessible data is available to support analytics, AI, and business initiatives.

Key Responsibilities:

  • Source, integrate, transform, and prepare data from complex enterprise data environments.
  • Develop and maintain scalable data pipelines, ETL/ELT processes, and data workflows.
  • Work extensively with Snowflake, Databricks, SQL, and SQL-based data warehouses.
  • Analyze enterprise data requirements and identify appropriate source systems, datasets, and data elements.
  • Enable data for downstream analytics, AI/ML, reporting, and business applications.
  • Investigate data quality issues, including discrepancies, missing data, duplicates, inconsistent values, mapping issues, and transformation errors.
  • Perform root-cause analysis and develop sustainable solutions to data quality and pipeline issues.
  • Validate data accuracy, completeness, consistency, and reliability across source and target systems.
  • Collaborate with data engineers, data scientists, AI teams, analysts, and business stakeholders to ensure data is fit for purpose.
  • Document data sources, transformations, business rules, data definitions, and quality requirements.
  • Support emerging AI/GenAI initiatives by ensuring enterprise data is properly sourced, structured, validated, and enabled.
  • Contribute to continuous improvements in enterprise data architecture, data quality, and data enablement practices.

Required Qualifications:

  • Strong professional background in Data Engineering.
  • Hands-on experience with Snowflake.
  • Hands-on experience with Databricks.
  • Strong SQL skills and experience working with SQL-based data warehouses.
  • Experience with enterprise-scale data sourcing, integration, transformation, and data pipelines.
  • Strong understanding of data quality, data validation, and troubleshooting.
  • Demonstrated ability to investigate data discrepancies and identify root causes.
  • Experience working with large, complex enterprise datasets.
  • Strong understanding of data enablement and making data accessible and usable for downstream consumers.
  • Ability to work effectively with technical and business stakeholders.

Preferred Qualifications:

  • Experience with Palantir.
  • Experience supporting AI/ML, Generative AI, or other AI initiatives.
  • Experience preparing enterprise data for AI/ML applications.
  • Knowledge of modern data architecture, data governance, and data management practices.
  • Experience with cloud data platforms and enterprise data ecosystems.

Ideal Candidate Profile:

The ideal candidate is a Data Engineer first, with strong hands-on experience in enterprise data sourcing and engineering, who has recently moved into or supported AI/ML and GenAI projects. You should be comfortable working deep in the data while understanding how high-quality enterprise data enables modern AI applications.

Apply