Top Data & Analytics Jobs in Dayton, OH

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Analyze contributor performance, dataset quality, production efficiency, and client delivery metrics. Build dashboards and reports, conduct cohort and trend analysis, monitor quality benchmarks, identify data integrity issues, and translate findings into actionable recommendations. Collaborate with Data Operations, Engineering, Product, and Quality Assurance teams to define metrics and evaluate improvements.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.

Creates, analyzes, edits, and maintains GIS data supporting fiber and HFC network planning, design, construction, and regulatory map updates. Uses ArcGIS and ArcFM to manage geodatabases, spatial datasets, parcel data, route assets, and network schematics. Conducts map research, interprets technical records, supports GIS projects, develops mapping standards, and responds to internal requests. Collaborates with legal, market expansion, engineering, construction, and design teams while ensuring geospatial data quality and alignment with GIS best practices.

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.