Top Analysis Reporting Jobs in Dayton, OH

Administer and optimize enterprise SQL Server and Azure SQL databases across development, testing, and production environments. Responsibilities include database installation, upgrades, migrations, modeling, security, backup and recovery, performance tuning, data warehouse and Power BI support, and ETL validation. The role collaborates with developers, stakeholders, vendors, and service providers while supporting releases, risk tracking, issue resolution, and process improvement.

Monitor prediction markets and perpetual futures by tracking news, trader sentiment, venues, volumes, open interest, funding rates, and market share. Research protocols and users, build dashboards and datasets, identify product opportunities for Pyth, size opportunities with market and on-chain data, and collaborate with product, engineering, and business development teams. Write research reports, present recommendations to leadership, and evaluate launched initiatives against their original theses.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Support Sales and Key Account teams by owning trade performance reporting, building dashboards, analysing promotional ROI, preparing category presentations for range reviews, and collaborating with Marketing, Supply Chain, and Finance to drive commercial decisions.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Develop and maintain BI reports and dashboards for the Mortgage Lending Division. Analyze large datasets, identify trends, validate reportable data, and support regulatory reporting such as HMDA and Fair Lending Analysis. Collaborate with business teams to gather requirements, automate reporting processes, document data definitions, and provide training on reporting tools. The role also supports data dictionaries, exception reporting, and operational improvements.

Develop and maintain Power BI, SSRS, SQL, recurring, and ad hoc reports for the Mortgage Lending Division. Analyze large datasets, validate regulatory reporting data, identify trends, automate reporting, and provide actionable insights. Collaborate with business units, support HMDA and fair lending reporting, document data dictionaries and procedures, and train users on reporting tools.