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Contractual Learning Analytics Jobs in Atlanta, GA

Guidehouse

Senior Data Analyst - Child Welfare

Atlanta, GA · On-site

$102K - $170K/yr

Based on our contractual obligations, candidate must be located within the United States and US ... Minimum 3 + years of experience in data analytics, business intelligence, or reporting * Proven ...

The South Plains

Network Manager

Atlanta, GA · On-site

... contractual information, and the review and analysis of reports as part of negotiation and ... We also offer numerous clinical and non-clinical learning opportunities and physician leadership ...

SANY America, Inc.

Warranty Analysis

Peachtree City, GA · On-site

Passion for continuous learning and knowledge accumulation, with the ability to think independently ... contractual obligations, and applicable regulatory standards. SANY America complies with applicable ...

Intuit

Head of Core & Premium Accountant Support

Atlanta, GA · On-site

$52K - $69K/yr

... contractual commitments, and a consistent performance management framework. * Directly manage an ... Drive the elevated-expertise transformation at scale: partner with Learning & Development ...

XTREME SOLUTIONS INC

Cybersecurity Auditor

Atlanta, GA · On-site

This role requires a meticulous and analytical professional capable of identifying vulnerabilities ... Review and validate compliance with organizational policies and contractual requirements. * Prepare ...

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Showing results 21-40

All JobsContractual Learning Analytics JobsContractual Learning Analytics Jobs in Atlanta, GA

Contractual Learning Analytics information

See Atlanta, GA salary details

$45.2K

$78.2K

$176.5K

How much do contractual learning analytics jobs pay per year?

As of Sep 7, 2026, the average yearly pay for contractual learning analytics in Atlanta, GA is $78,248.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,800.00 and $85,600.00 per year, depending on experience, location, and employer.

What is contractual learning analytics?

Contractual Learning Analytics refers to the use of data analysis and measurement tools to evaluate the effectiveness of educational programs as defined within a specific contract or agreement. Professionals in this field collect, interpret, and report on learning data to ensure that educational outcomes meet the expectations outlined in learning contracts, such as those between educational institutions and third-party providers. They focus on metrics like student performance, engagement, and satisfaction, providing evidence-based insights to inform decision-making and contract fulfillment. This role often requires collaboration with educators, administrators, and technology specialists to optimize learning experiences and demonstrate value.

What are the key skills and qualifications needed to thrive as a contractual learning analytics professional?

To thrive as a Contractual Learning Analytics professional, you need strong analytical skills, experience with educational data, and typically a degree in data science, education, or a related field. Proficiency in learning management systems (LMS), data visualization tools like Tableau or Power BI, and statistical software such as R or Python is often required. Excellent communication, problem-solving abilities, and adaptability help in interpreting data insights and collaborating with stakeholders. These skills are crucial for driving data-informed decisions that enhance educational outcomes and meet organizational goals.

What are some common challenges faced in a contractual learning analytics role, and how can they be managed?

In a contractual learning analytics role, one frequent challenge is quickly adapting to different organizational cultures and data systems, as each contract may involve unique platforms and reporting requirements. Effective communication with instructional designers and educators is essential to ensure analytics align with learning objectives. Managing time efficiently to deliver actionable insights within tight deadlines is also crucial. Building a strong rapport with stakeholders at the start of each contract and maintaining flexibility in analytical approaches can help overcome these challenges and contribute to successful project outcomes.

What is the difference between Contractual Learning Analytics vs Data Analyst?

AspectContractual Learning AnalyticsData Analyst
CredentialsTypically requires degrees in education, data science, or related fields; certifications in analytics or learning technologiesRequires degrees in statistics, mathematics, or related fields; often certifications in data analysis tools
Work EnvironmentEducational institutions, e-learning companies, or corporate training providersVarious industries including finance, healthcare, marketing, and technology
Employer & Industry UsageUsed by educational organizations to improve learning outcomes and curriculum designUsed across industries to interpret data, inform business decisions, and optimize processes

Contractual Learning Analytics focuses on analyzing educational data to enhance learning experiences, often within educational or training settings. Data Analysts have a broader scope, working across multiple industries to interpret data for strategic decision-making. While both roles require strong analytical skills, their specific applications and environments differ significantly.

What are popular job titles related to Contractual Learning Analytics jobs in Atlanta, GA?

For Contractual Learning Analytics jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Contractual Learning Analytics jobs in Atlanta, GA look for?

The top searched job categories for Contractual Learning Analytics jobs in Atlanta, GA are:

Contractual Learning Analytics jobs near you

Databricks Practice Lead / Engineering Manager

Scicom Infrastructure Services, Inc.

Atlanta, GA • On-site

$98K - $129K/yr

Contractor

Re-posted 18 days ago

Key responsibilities

  • Design and oversee modern data platforms using Databricks, including architecture, standards, and best practices.

  • Manage, mentor, and develop a team of data engineers, architects, and consultants supporting enterprise and government programs.

  • Provide delivery oversight for Databricks projects, translating requirements into technical plans and ensuring successful implementation.

Job description

Position Summary
Scicom Infrastructure Services is seeking an experienced Databricks Practice Lead / Engineering Manager to provide hands-on technical leadership while managing a team of data engineers, architects, and consultants supporting complex enterprise and government programs.
This role requires a senior Databricks expert who can design and oversee modern data platforms, establish technical standards, guide delivery teams, and remain actively involved in architecture, troubleshooting, code reviews, and client-facing solution development. The successful candidate will balance deep technical expertise with strong people leadership, delivery management, and stakeholder communication skills.
Key Responsibilities
Databricks Technical Leadership
  • Serve as the organization's subject-matter expert for the Databricks Lakehouse Platform.
  • Design scalable, secure, and highly available data architectures using Databricks, Apache Spark, Delta Lake, and cloud-native technologies.
  • Lead the implementation of batch, streaming, ETL, ELT, analytics, machine-learning, and AI-enabled data solutions.
  • Define architectural standards for medallion architectures, data modeling, ingestion, transformation, orchestration, and data consumption.
  • Establish governance frameworks using Unity Catalog, including data lineage, access controls, auditing, metadata management, and secure data sharing.
  • Guide Databricks workspace design, cluster configuration, serverless computing, workload isolation, performance tuning, and cost optimization.
  • Oversee integration between Databricks and cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
  • Develop or review solutions involving PySpark, Spark SQL, Python, Delta Live Tables, Structured Streaming, Auto Loader, MLflow, and Databricks Workflows.
  • Lead platform migrations and modernization efforts from legacy databases, data warehouses, Hadoop environments, and traditional ETL platforms.
  • Establish development standards for source control, automated testing, CI/CD, infrastructure as code, monitoring, and production support.
  • Conduct architecture reviews, code reviews, technical assessments, and root-cause analyses.
  • Evaluate emerging Databricks capabilities and recommend appropriate adoption strategies.

Team Leadership and Management
  • Manage, mentor, and develop a team of Databricks engineers, data engineers, architects, and technical consultants.
  • Assign resources and responsibilities based on project needs, employee strengths, availability, and technical complexity.
  • Establish measurable goals, performance expectations, development plans, and technical competency standards.
  • Conduct regular one-on-one meetings, performance reviews, coaching sessions, and technical development activities.
  • Support recruiting, interviewing, candidate evaluation, onboarding, and workforce planning.
  • Identify technical or performance gaps and coordinate training, mentoring, or corrective action as appropriate.
  • Promote collaboration, accountability, documentation, knowledge sharing, and continuous improvement.
  • Develop reusable accelerators, reference architectures, templates, and delivery playbooks.
  • Build and maintain a strong Databricks practice capable of supporting multiple concurrent client engagements.

Program and Delivery Management
  • Provide delivery oversight for Databricks and data-engineering projects from planning through implementation and operational support.
  • Translate business, functional, security, and contractual requirements into technical plans and deliverables.
  • Develop project estimates, staffing plans, delivery schedules, milestones, and risk-mitigation strategies.
  • Monitor project scope, schedule, quality, budget, resource utilization, dependencies, and technical risks.
  • Ensure deliverables meet client requirements, internal quality standards, security controls, and contractual commitments.
  • Coordinate work across engineering, cloud, cybersecurity, data governance, analytics, project-management, and client teams.
  • Track delivery metrics and provide clear status reports to internal leadership, clients, and program stakeholders.
  • Lead technical escalations and ensure issues are resolved promptly and appropriately documented.
  • Support statements of work, technical proposals, solution estimates, presentations, and client demonstrations.
  • Participate in client meetings as the technical and delivery authority for Databricks-related work.

Required Qualifications
  • Bachelor's degree in computer science, information technology, data engineering, engineering, or a related discipline.
  • At least 10 years of experience in data engineering, data architecture, analytics engineering, or related technology roles.
  • At least 5 years of hands-on experience designing and implementing solutions using Databricks.
  • At least 3 years of experience managing or formally leading technical engineering teams.
  • Advanced experience with:
    • Databricks Lakehouse Platform
    • Apache Spark and PySpark
    • Spark SQL and advanced SQL development
    • Delta Lake and medallion architecture
    • Unity Catalog and enterprise data governance
    • ETL and ELT pipeline architecture
    • Batch and real-time data processing
    • Data modeling and data warehousing
    • Python-based data engineering
    • Databricks Workflows, Jobs, and cluster management
  • Experience deploying Databricks solutions in Azure, AWS, or Google Cloud.
  • Experience with CI/CD, Git-based development, automated testing, and infrastructure as code.
  • Demonstrated ability to optimize Spark workloads, cluster configurations, query performance, reliability, and cloud costs.
  • Experience managing technical delivery, resource assignments, risks, schedules, and client expectations.
  • Strong written, verbal, presentation, documentation, and stakeholder-management skills.
  • Ability to explain complex technical concepts to executives, business stakeholders, and nontechnical audiences.

Preferred Qualifications
  • Databricks Certified Data Engineer Professional, Databricks Certified Data Engineer Associate, or Databricks Certified Machine Learning Professional.
  • Databricks Certified Data Architect or comparable advanced architecture credentials.
  • Microsoft Azure, AWS, or Google Cloud professional-level certification.
  • Experience working in a consulting, professional-services, systems-integration, or managed-services environment.
  • Experience supporting federal, state, or local government clients.
  • Experience working with major consulting or systems-integration partners.
  • Knowledge of federal security, privacy, governance, and compliance requirements.
  • Experience with Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, AWS Glue, Amazon S3, Snowflake, dbt, Kafka, Airflow, or Terraform.
  • Experience with MLflow, MLOps, generative AI, Databricks Mosaic AI, vector search, or machine-learning deployment.
  • Familiarity with data standards, metadata frameworks, data catalogs, data-sharing protocols, and open-data environments.
  • Experience managing geographically distributed or remote technical teams.
  • Experience contributing to proposals, technical responses, statements of work, and project estimates.

Leadership Competencies
The successful candidate will demonstrate:
  • Hands-on technical credibility and sound architectural judgment.
  • The ability to lead without becoming disconnected from the technology.
  • Strong accountability for team performance and project outcomes.
  • Effective coaching, delegation, and conflict-resolution skills.
  • Clear and proactive communication with clients and internal leadership.
  • The ability to manage competing priorities in a fast-paced consulting environment.
  • A commitment to quality, security, documentation, and continuous improvement.

Success Measures
Performance in this role will be evaluated based on:
  • Quality, scalability, security, and reliability of Databricks solutions.
  • On-time and within-budget delivery of client commitments.
  • Team performance, retention, development, and technical growth.
  • Client satisfaction and effective stakeholder communication.
  • Reduction in delivery risks, production incidents, and technical debt.
  • Adoption of standardized architectures, engineering practices, and reusable solutions.
  • Effective management of Databricks consumption, infrastructure, and cloud costs.
  • Growth and maturity of the organization's Databricks practice.

Apply