Top Analysis Reporting Jobs in Little Rock, AR

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Develop and optimize SQL Server and SSIS billing pipelines, automate data movement and validation with Python, and use R and Python for analytics, anomaly detection, and billing decision models. Build data-quality frameworks, support internal reporting tools, integrate ERP APIs, and collaborate with finance and operations stakeholders. The role requires maintainable, tested, auditable solutions and ownership of features across databases, pipelines, analytics, and internal applications.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Lead product experimentation and analytics across three product squads. Design experiments, define product metrics, analyze patient and clinician behavior, build dashboards, apply causal inference, and communicate actionable insights. Partner with product, engineering, design, clinical, operations, and data teams to improve instrumentation, data quality, and evidence-based healthcare product decisions.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Analyze qualitative and quantitative customer feedback, behavioral data, and operational metrics to identify user pain points and root causes. Develop dashboards, KPIs, reporting, and measurement frameworks; quantify experience impacts; and provide evidence-based recommendations. Partner cross-functionally with Product, Engineering, Marketing, Data, and Support leaders to influence prioritization, evaluate initiatives, and improve customer satisfaction, usability, loyalty, adoption, and support demand.

Leads enterprise data architecture for Customer 360, CDP, identity resolution, marketing, personalization, real-time activation, paid media, measurement, and AI capabilities. Designs unified data models, streaming and batch integrations, governed activation patterns, data contracts, APIs, observability, and security controls. Partners with business and technical leaders on roadmaps, architecture reviews, vendor evaluations, and implementation decisions while mentoring engineering teams and ensuring privacy, compliance, and responsible AI standards.

Designs and leads scalable, secure, and cost-efficient Snowflake data platforms supporting enterprise analytics, operational reporting, and generative AI applications. Serves as the technical authority for data architecture, modeling, lakehouse design, Snowflake services, dbt transformation frameworks, and Python/Snowpark development. Provides hands-on architectural leadership while influencing executive-level technology strategy.