Top Remote Analysis Reporting Jobs in Philadelphia, PA

Designs, develops, and maintains scalable cloud-based data pipelines and enterprise data solutions. The role covers data ingestion, transformation, orchestration, integration, data lakes, cloud warehouses, batch and streaming processes, performance optimization, governance, and lineage. The Senior Data Engineer collaborates with analysts, data scientists, engineers, stakeholders, contractors, and vendors while mentoring junior staff. Core tools include Databricks, dbt, Azure Data Factory, SQL, Python, and Azure.

Executes and monitors scheduled production data and data exchange processes for internal and external customers. Follows and improves SOPs, troubleshoots issues, monitors resolution progress, communicates results, supports outages and patch validation, and balances competing requests. The role requires SQL and Control-M expertise, healthcare technology experience, independent judgment, customer communication, and availability for 24x7 shifts.

Analyze startup user and product data, identify trends and drivers, and turn findings into strategic recommendations. The role owns key metrics and dashboards, leads analytical deep dives, sizes opportunities, evaluates product tradeoffs, supports roadmap prioritization, and develops external-facing insights about startups in partnership with marketing and communications.

Conduct geospatial and molecular HIV data analysis using R, SQL, or Python; manage, merge, validate, and document datasets; support data governance, reporting, and dissemination; and develop or maintain Power BI dashboards. Collaborate with analysts, review data quality issues and error logs, document corrections, and provide project updates while supporting CDC public health surveillance initiatives.

Support enterprise asset management data migration and system integration initiatives involving IBM Maximo. Analyze source and target environments, execute migration strategies, develop migration and interface components, validate data exchanged with enterprise platforms, and conduct quality reviews. Coordinate migration workstreams and help implement data governance standards. The role requires Maximo Integration Framework experience, large-scale data management expertise, and eligibility for Public Trust clearance.

Analyze and model marketing, product, and loan application data to optimize the borrower funnel and drive growth. Build BigQuery datasets, dbt models, and Tableau dashboards; evaluate customer behavior, acquisition costs, approval rates, and origination performance. Partner with Product, Marketing, and Engineering on A/B tests, statistical analysis, and actionable insights for leaders. Apply lending and fintech knowledge to improve conversion, customer value, and portfolio outcomes.

Develop and maintain Tableau dashboards, data visualizations, analytics solutions, and reporting tools. Clean and transform data using Databricks, SQL, and Python; identify workflow automation opportunities; and partner with stakeholders to deliver analytics and AI use cases. The role also involves client presentations, requirements gathering, business development support, and federal government consulting.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Own the architecture and operation of 9amHealth’s data platform, including Python and SQL ETL/ELT pipelines, AWS infrastructure, Redshift data models, semantic layers, governance, quality, monitoring, and reliability. Establish technical standards, optimize performance and costs, support AI/ML initiatives, build integrations, and mentor data team members. Partner with business and clinical stakeholders to deliver reliable data products and translate complex questions into actionable analytics.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.