Top Remote Analysis Reporting Jobs in Cincinnati, OH

Build and operate the Defense & Systems Data Fabric through metadata ingestion pipelines, API and connector integrations, data-product onboarding, and policy enforcement. Implement policy-as-code and attribute-based access control for export-control and data-sovereignty requirements. Maintain monitoring, alerting, reliability, documentation, and certification workflows across hybrid cloud, on-premises, and edge environments while conforming to architectural and governance standards.

Leads decision support and data analysis projects, translating business requirements into reporting applications and analytical solutions. Develops complex programs using SAS, R, SQL, and database technologies; models data sources and flows; creates analytical methods and performance measures; interprets statistical results; and presents recommendations to stakeholders. Mentors colleagues, supports data governance and security frameworks, and consults with internal and external business partners.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, ambiguous environment.

Build and operate large-scale data pipelines that acquire, process, and improve audio datasets for AI model training. The role focuses on data quality, throughput, cost optimization, production systems, and collaboration with AI scientists. Responsibilities include discovering data sources, developing reliable processing systems, and owning engineering projects from discovery through continuous improvement.

Designs and maintains production data ingestion pipelines, layered analytics-ready data models, and reliable automated data foundations. The role integrates client data from APIs, files, Databricks, and shared-data sources; implements testing, monitoring, schema-change handling, and safe reprocessing; and supports application, analytics, and future AI use cases. It also requires collaboration with product teams, strong communication, cloud data warehouse experience, and AI-native development practices.

Reviews and annotates video and audio clips, evaluates AI-generated multimedia descriptions, moderates content against project guidelines, and provides detailed written feedback. The role requires consistent high-volume data labeling, confidentiality, independent remote work, collaboration, and participation in process improvements.

Reviews short video and audio clips, annotates visual and auditory content, evaluates AI-generated descriptions, and provides clear written feedback. Applies project guidelines consistently, flags unclear or noncompliant content, maintains data quality and confidentiality, collaborates remotely with project coordinators, and suggests improvements to annotation and quality assurance processes.

Designs and maintains secure, reliable data platforms, including relational data models, migrations, pipelines, monitoring, data quality processes, and self-service reporting access. The role protects PHI and PII through access controls, encryption, audit logging, and governance in a HIPAA environment. It also involves backend API development, code reviews, security reviews, stakeholder collaboration, and mentoring teams on data practices.

Build and maintain reconciliation data pipelines, dbt models, matching logic, anomaly detection, monitoring, and exception-handling systems for financial transactions. Own data definitions, lineage, testing, and quality standards while automating manual workflows. Partner with Engineering, Finance, Accounting, and Operations to solve complex transactional-data problems and apply practical AI and machine learning techniques.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.

Analyze behavioral, transactional, and interaction data to identify fraud patterns and emerging threats. Develop monitoring frameworks, dashboards, and mitigation solutions; evaluate AI fraud-review decisions; and translate findings into prompt, rule, workflow, and machine-learning feature improvements. Collaborate with Fraud Operations and Machine Learning teams, participate in code reviews, and mentor junior analysts. The role requires strong SQL and Python skills, statistical analysis expertise, business judgment, and fraud analytics experience.