Top Data & Analytics Jobs in Philadelphia, PA

Analyzes customer, operational, and financial data to produce dashboards, predictive insights, value cases, and actionable recommendations. Partners with cross-functional leaders to improve customer experience, field service, scheduling, care, operational performance, and margins. Maintains KPI definitions, supports business reviews, improves data quality, and tracks initiatives from recommendation through realized impact. Uses AI-enabled tools responsibly to accelerate analysis and develop agent-assisted workflows.

Leads enterprise-wide data and AI governance, including policy development, AI Governance Center of Excellence leadership, risk assessment, regulatory compliance, data stewardship, and governance lifecycle implementation. Partners with Engineering, Product, business units, and executives to manage data quality, metadata, master data, retention, privacy, bias, cybersecurity, and third-party risks. Establishes governance KPIs, advises senior leadership, and promotes responsible AI accountability through training and awareness programs.

Owns end-to-end architecture and delivery of data products, including schemas, entity resolution, provenance, schema evolution, and normalization pipelines. Leads conversion of unstructured and semi-structured sources into enterprise-quality datasets using deterministic parsing, LLM-assisted extraction, inference, and human review. Partners with domain experts, data scientists, and AI engineers while applying strong SQL, Python, distributed processing, testing, observability, and CI/CD practices.

The Junior Data Analyst cleans, transforms, validates, queries, and maintains data for business intelligence. Responsibilities include database management, data quality assurance, statistical analysis, dashboard and report development, documentation, stakeholder collaboration, SOP support, and contributing to data lake or warehouse solutions. The role requires SQL, Excel, strong analytical skills, and the ability to support data-driven decisions in a fully on-site environment.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Leads the organization’s data strategy, analytics, and measurement capabilities. Advises enterprise clients on attribution, incrementality, marketing mix modeling, customer acquisition, and lifetime value. Oversees analytical workstreams, data products, dashboards, measurement tools, and the analytics roadmap while partnering with strategy, sales, client services, and AI teams. Builds data literacy through training and thought leadership, mentors talent, supports commercial growth, and represents the company at industry events.

Design, build, and operate scalable batch, streaming, and CDC data pipelines across Snowflake and AWS. Lead ETL/ELT architecture, orchestration, data modeling, quality monitoring, observability, incident resolution, and performance optimization. Develop production solutions in SQL and Python, establish engineering standards for testing and CI/CD, and partner with Analytics Engineering, Data Science, Product, and Engineering teams to deliver reliable data products.

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.

Leads the Underwriting Data Engineering team and oversees the design, development, and productionization of scalable data and machine learning pipelines. Responsibilities include feature engineering, data preparation, model deployment, data validation, governance, monitoring, API integration, analytic data products, roadmap development, stakeholder collaboration, and team coaching.

Designs, develops, and owns business intelligence dashboards, visualizations, analyses, and reporting solutions, primarily in TIBCO Spotfire and also Power BI. Responsibilities include data modeling, calculations, performance tuning, deployment workflows through GitHub, data quality collaboration, BI standards, risk identification, mentoring, and independent decision-making across a multi-tenant Life Sciences data platform.