Top Data & Analytics Jobs in Philadelphia, PA

Own supervisory data management and governance as a subject matter expert. Define data quality rules, metrics, monitoring, issue remediation, taxonomies, metadata, and cataloging practices. Partner with business, IT, and platform teams to improve data reliability, accessibility, lifecycle management, and future AI readiness. Translate business needs into technical requirements, facilitate adoption of governance practices, and communicate recommendations to technical and executive audiences.

Designs and develops scalable data architectures, pipelines, data warehouses, and analytics solutions across on-premise, cloud, and hybrid environments. Performs data wrangling and discovery, collaborates on business intelligence initiatives, mentors data engineers, contributes to technical capability development, and supports project planning and estimation.

Analyzes operational performance metrics, generates recurring and ad hoc reports, audits data integrity, identifies process issues, and recommends improvements. The role builds reporting tools, consolidates data, compares performance against budgets, evaluates strategic proposals, and provides analytical insights to support business decisions. It requires independent judgment, collaboration with management, and flexibility to work nights, weekends, and variable schedules.

Owns data systems workstreams for public fixed income investment operations. Leads process improvement and automation, develops reports, dashboards, and data models, manages data quality and issue resolution, and performs portfolio analysis, forecasting, benchmarking, and financial modeling. Partners with investment, client, and business teams on reporting and analytics while supporting technology implementations, translating business needs into technical requirements, and mentoring junior analysts.

Leads data validation risk initiatives, strategic analytics direction, business development, and complex data quality programs across client engagements. Oversees data collection, preprocessing, modeling, statistical analysis, validation routines, and control-gap assessments. Translates findings into executive recommendations, manages senior client relationships, mentors analytical teams, and advances data governance and AI-enabled analytics while ensuring professional and independence standards.

Develop and improve IP geolocation automation algorithms using large datasets and IP network topology. Query, extract, and transform data with SQL, R, Python, MongoDB, and BigQuery; provide insights that improve data quality; and collaborate with engineers, data scientists, and stakeholders to solve complex problems and lead data science initiatives.

The Quantitative Methodologist will design statistically rigorous studies, apply advanced analytics, interpret results, ensure quality, and communicate insights to clients.

Builds, tests, implements, documents, and supports business intelligence solutions, including reports, dashboards, semantic layers, metadata, stored procedures, and database services. Partners with healthcare operational and business leaders to translate data into actionable insights, answer business questions, support KPI-driven decisions, enable analytics-tool adoption, troubleshoot BI systems, tune performance, and provide occasional on-call support.

Analyzes supply chain, logistics, inventory, and distribution data; builds BI dashboards, KPI models, and recurring reports; validates data; performs root-cause analysis; and delivers actionable insights to improve inventory health, service levels, fulfillment, and supply continuity. Partners with operational stakeholders to define metrics, standardize reporting, automate processes, and support optimization initiatives.

Build workflow-specific data pipelines, integrations, and data models supporting AI solutions. Map data across systems, files, emails, and spreadsheets; transform unstructured documents into structured data; establish data quality checks; resolve data issues; and contribute reusable workflow data to the shared AI platform.

Build and maintain production ETL pipelines and scalable PySpark jobs on Databricks. Develop complex SQL transformations across SQL Server, Synapse Analytics, and Snowflake; collaborate with data scientists; monitor pipeline health; improve data quality and observability; document data flows; present technical work to stakeholders; and partner with Azure and platform engineering teams.