Top Data & Analytics Jobs in Little Rock, AR

Designs, develops, and maintains scalable ETL pipelines, data lakes, warehouses, and integrated data solutions. The role includes requirements analysis, Agile collaboration, testing, debugging, documentation, cloud data engineering, and optimization of reliable and performant systems. It requires strong Python, SQL, database, distributed processing, data engineering, software development, and cloud platform skills, along with effective communication and problem-solving abilities.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Lead fraud analytics for application fraud detection: identify emerging patterns, design and test real-time defenses, balance risk and customer experience, build dashboards and metrics, and collaborate with Operations, Product, Data Science, and Engineering to reduce fraud losses and improve cardholder experience.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Build and extend automated enterprise data governance and quality frameworks. Define measurable trust rules, data contracts, validation and certification workflows, metadata, lineage, tagging, and trust scoring across a cloud data platform. Integrate governance checks into developer workflows and Collibra, coach engineering and product teams, and establish organization-wide data trust standards. The role requires strong SQL, BigQuery or comparable warehouse experience, and expertise with data quality or observability tools.

Analyzes, cleanses, validates, and prepares operational datasets for mailing, email, reporting, billing, and processing activities. Develops Databricks queries and Alteryx workflows to automate processes and improve data quality. Performs audits, reconciliations, root-cause analysis, and corrective actions while maintaining governance and security compliance. Provides actionable insights, mentors junior analysts, communicates findings, and leads continuous improvement initiatives.

Analyze platform, product, customer, advertising, and operational data to generate actionable insights. Build dashboards, reporting frameworks, automated workflows, and scalable analytics pipelines. Evaluate experiments and analytical methodologies, improve forecasting and data accuracy, and partner with product, engineering, marketing, revenue, and leadership teams to influence strategy, customer experience, and platform growth.

Analyzes consumer, shopper, and market data to identify trends, risks, and growth opportunities. Translates complex findings into presentations, visualizations, recommendations, and client-ready insights. Supports client meetings, cross-functional solution development, relationship building, and engagement expansion while developing expertise in Circana’s data assets and platforms.

Lead data engineering initiatives that build scalable warehouses, pipelines, ETL/ELT processes, dashboards, and automated reporting workflows. Translate finance and operational needs into reliable data solutions, implement governance and quality practices, and support integrations, migrations, acquisitions, and system enhancements across portfolio companies. Partner directly with finance, accounting, analytics, and operational stakeholders to improve decision-making.

Manage MSSQL and PostgreSQL databases, including installation, configuration, monitoring, performance tuning, security, backup and recovery. Develop optimized SQL queries, stored procedures, triggers, and views. Use AWS services for database deployment and scaling, Git/GitHub for version control, and collaborate with developers and system administrators. Support data warehouse design, ETL processes, reporting requirements, and database troubleshooting.