Top Data & Analytics Jobs in Little Rock, AR

Supports workforce management through forecasting, scheduling, shrinkage and headcount analysis, data validation, call routing adjustments, and service-level monitoring. Develops reporting requirements, dashboards, scorecards, and performance insights; recommends staffing and process improvements; documents procedures and training materials; and troubleshoots workforce management systems while escalating issues as needed.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Analyze and tune sanctions, fraud, and AML models; evaluate performance, reduce false positives, develop features and datasets, automate analysis, troubleshoot data issues, and communicate findings to technical, compliance, regulatory, and senior management stakeholders.

Build and maintain scalable dbt-based data models and analytics-ready datasets for pharmacy benefit management and care navigation. Translate healthcare workflows into auditable data products, implement data quality testing and reconciliation, optimize warehouse performance, and establish documentation, lineage, governance, CI/CD, and observability practices. Collaborate with technical and business teams to support reporting, financial analysis, self-service BI, and future AI-enabled analytics capabilities.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Lead complex customer analytics engagements, translating industrial business needs into scalable analytics architectures and solutions. Advise customers, drive product adoption, identify high-value use cases, mentor Analytics Engineers, resolve technical escalations, and influence product roadmaps. Apply expertise in industrial process data and sectors such as power, pharmaceuticals, and semiconductors. Collaborate with customers, partners, product teams, and account teams to deliver measurable business outcomes.

Lead enterprise Master Data Management strategy, architecture, governance, data quality, and integration initiatives across customer, product, supplier, employee, and financial domains. Design data models, hierarchies, matching and survivorship rules, integration patterns, and implementation roadmaps. Advise clients on Oracle and Salesforce ecosystems, lead workshops and executive presentations, guide solution delivery, mentor consultants, and contribute to proposals and statements of work.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data warehouses supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains SSRS reports, implements data quality controls, migrates legacy systems, and collaborates with business and technical teams. The role also promotes AI-assisted development practices and documents technical procedures.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.