Top Data & Analytics Jobs in Dayton, OH

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Evaluates and compares chatbot responses against business requirements and quality criteria. Gathers requirements, documents functional specifications, analyzes workflows and data, creates reports and dashboards, supports testing and UAT, tracks project risks and KPIs, and presents evidence-based findings. Collaborates with technical teams and stakeholders to improve AI output quality while maintaining evaluation consistency and meeting deadlines.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Develops dashboards, data models, reports, and analytical solutions that transform raw data into actionable business insights. Partners with stakeholders, provides technical guidance, documents architectures and processes, troubleshoots analytics solutions, and communicates findings to technical and non-technical audiences. Requires advanced Spotfire, SQL, business intelligence, and data analytics expertise, with knowledge of Databricks, Power BI, Python, R, Salesforce, and modern data architecture.

Owns Power BI reporting and analytics for Digital, including internal dashboards, campaign pacing, client-facing intelligence products, contact data health, and data delivery monitoring. Builds DAX reporting logic, automated alerts, budget tracking, segmentation, benchmarking, and trend analysis. Partners with IT, Data Engineering, CRM, and audience teams while transitioning an externally developed platform in-house. Serves as the senior reporting subject matter expert for stakeholder and client-facing analytics.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Leads advanced analytics across the organization, owning MAU forecasting and growth models, experimentation standards, and causal inference methods. Establishes analytical best practices, reviews high-stakes analyses, partners with data and product teams, and scales recurring work through AI tooling. Mentors data professionals and translates complex statistical findings into actionable leadership recommendations.

Lead the enterprise data governance program, roadmap, operating model, policies, standards, governance bodies, and owner/steward accountability. Build data domains, taxonomies, contracts, lineage, metadata, and audit controls across complex multi-brand platforms. Partner with Technology, Legal, Privacy, Security, Risk, Compliance, and business leaders to ensure trustworthy data for analytics and AI. Manage governance leads and communicate strategy, risks, and recommendations to senior executives and auditors.