Top Data & Analytics Jobs in Philadelphia, PA

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Design and maintain conceptual, logical, and physical Medicaid data models using ERwin or ER/Studio. Develop a scalable Medicaid data warehouse structure, support data catalog and lineage creation, map system interactions, and identify data quality, integration, governance, security, and database design improvements. Collaborate with state Medicaid and IT staff, document data movement and metadata, contribute to status reporting, and work onsite in Sioux Falls for the engagement.

Owns advanced mobile and web analytics for the NPR App, including complex SQL analysis of raw BigQuery, GA4, and Firebase data. Leads A/B and multivariate experimentation, translates behavioral insights into product strategy, develops reporting and self-service tools, ensures data quality, and mentors colleagues and stakeholders on SQL, analytics, experimentation, and data governance.

Designs and refines Microsoft Fabric architectures, builds metadata-driven ingestion frameworks, migrates Azure Synapse workloads, and develops PySpark and Spark SQL pipelines. The role includes data validation, reconciliation, CDC strategies, medallion architecture, Git-based CI/CD, governance and lineage assessment, and AI-assisted development with Claude Code. The consultant collaborates directly with client engineers through paired development, code reviews, architecture decisions, and knowledge sharing.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.

Owns B2B pipeline and B2C growth analytics, including attribution, acquisition, registration funnels, organic search, forecasting, dashboards, experimentation, and marketing measurement. Partners with marketing, sales, product, data engineering, and leadership to define metrics, improve tracking, deliver actionable insights, and support data-driven decisions. The role also contributes to dbt models, semantic-layer development, analytical quality standards, and mentoring.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, support CRM and marketing automation configuration, and document data models and processes. Use AI tools such as Claude to accelerate analytics workflows while ensuring reporting accuracy. Partner with Marketing Analytics, Demand Generation, and Marketing Operations to support executive-level pipeline reporting.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.