Top Entry Level Analysis Reporting Jobs in Philadelphia, PA

Develop and maintain Power BI reports, dashboards, and data models using SQL and DAX. Analyze large datasets to identify trends, resolve reporting and data issues, ensure data accuracy, and provide actionable insights for business decisions. Collaborate with leadership and stakeholders to prioritize reporting needs, support users, and translate technical findings into clear business recommendations.

Analyzes financial, billing, and operational data for ancillary revenue programs, developing dashboards and reporting to track adoption, performance, revenue impact, and billing accuracy. Identifies process inefficiencies, supports forecasting and post-launch monitoring, resolves billing discrepancies with Accounting, and coordinates documentation and communication across departments. The role provides actionable insights and operational support for program execution, acquisitions, dispositions, and new revenue initiatives.

Leads enterprise data architecture, integration, governance, and system connectivity across commercial and education platforms. Oversees databases, data systems, security, compliance, performance, scalability, and reliability. Translates business needs into technical solutions, drives data initiatives and continuous improvement, establishes data standards, troubleshoots system issues, and mentors stakeholders and team members. The role requires cross-functional collaboration across technology, commercial, and education functions in a regulated MedTech environment.

The IT Business Analyst gathers and documents business requirements, maps processes and data flows, supports testing and change control, and bridges stakeholders with software engineering teams. The role supports SDLC delivery, facilitates workshops, prepares user stories and acceptance criteria, analyzes systems for optimization opportunities, and develops documentation, dashboards, reports, presentations, and training materials. It also supports the PMO and ensures compliance with City, aviation, cybersecurity, accessibility, and record-keeping standards.

Conduct quantitative equity research and analysis, maintain and reoptimize multifactor stock-selection models, develop investment tools, prepare reports and charts, manage quantitative data, investigate AI applications, and support investment strategy and process improvements.

Build and maintain production knowledge graphs in Stardog using semantic web standards, ontology modeling, SPARQL, virtual mappings, Python, and SQL. Integrate life science data sources, implement data quality and validation, automate CI/CD deployments, support governance and lineage, and collaborate with subject matter experts to develop defensible semantic models.

Performs quantitative catastrophe risk analyses using existing models to estimate client exposure and potential losses from natural and man-made catastrophes. Reviews and validates client exposure data, identifies abnormalities, prepares reports and presentations, communicates with clients on routine projects, and researches scientific and industry information on regional perils.

Analyzes investment data and operational processes, resolves data-quality issues, develops metrics and dashboards, documents workflows, and supports project coordination. The role partners with investment, technology, operations, and external stakeholders on process improvement, change management, risk mitigation, and operational transitions. It communicates findings, tracks project deliverables, performs root-cause analysis, and improves data quality and operational effectiveness.

Build AI-assisted interactive data visualizations, calculators, simulations, and internal web applications using Claude Code, React, and live data integrations. Translate ambiguous stakeholder needs into prototypes, then deploy and improve production-quality tools with reliable data pipelines, authentication, and performance. Collaborate with data engineering, establish reusable development patterns, train analysts on code-first workflows, and replace legacy BI dashboards with purpose-built applications.

Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, Snowflake, and Azure technologies. Build data ingestion frameworks, dimensional data models, and enterprise warehouse solutions. Optimize Snowflake workloads, implement data quality and governance controls, monitor production pipelines, troubleshoot incidents, document data processes, and collaborate with architects, analysts, data scientists, and business stakeholders. Support secure, compliant, reliable data platforms for analytics, reporting, and operational use cases.