Top Entry Level Data & Analytics Jobs in Philadelphia, PA

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.

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.

The SQL Developer will support the full project lifecycle, writing SQL procedures, improving database performance, and maintaining database objects.

The Big Data Analyst will analyze complex data, communicate findings, work with teams, and solve problems related to BI and case management.