Top Analysis Reporting Jobs in Baltimore, MD

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Lead research and listening for a specific audience: design and run primary research, integrate listening signals, synthesize quantitative and qualitative insights, and deliver timely, decision-oriented recommendations while coaching analysts and partnering with enterprise stakeholders.

Lead BI efforts by collecting, integrating, analyzing, and visualizing complex internal and external data. Advise executives and develop functional strategies, translate requirements across business and technical teams, lead projects, present insights to senior leadership, and build analytics solutions using tools like Power BI, Adobe Analytics, and Databricks.

Analyze large pharmacy, Medicaid, and Medicare datasets to produce actionable insights. Build and maintain dashboards and visualizations, investigate trends and root causes, recommend process improvements, partner with cross-functional teams, ensure data integrity, and influence strategy through data-driven recommendations to improve pharmacy operations and member experience.

Build scalable GTM and product analytics to inform revenue, retention, and growth decisions. Partner with Sales, Customer Success, Finance, Product, and Data Engineering to deliver dashboards, models, and executive data stories; validate data quality; measure ROI; and enable self-service reporting across the lead-to-quote-to-cash journey.

Maintain and configure client coding and reporting tables, audit accruals, create and transmit standard and government reports, update coding to ensure payroll/PSL data consistency, and define client requirements for custom reporting while collaborating with clients and internal teams.

Design, build, and maintain ETL/ELT pipelines and analytics data models from structured and event-level data. Transform clickstream/telemetry into user-level journeys, implement dbt transformations in a cloud warehouse (Redshift/Snowflake/BigQuery), develop reverse ETL workflows, enforce CI/CD/version control and automated data quality tests, and partner with BI, marketing, and product to deliver dashboards and attribution reporting.

Lead healthcare payment integrity analyses, collaborate with clients and internal teams, build and maintain large-scale SQL and Spark queries, design ETL/ELT transformations, implement AI/LLM-driven analytical workflows, perform data quality monitoring, and mentor analysts while documenting lineage and audit-ready methodologies.

Design and maintain a formal analytics data layer (AWS Glue, Athena/Presto, LookML); build data models, pipelines, and automated quality checks; investigate anomalies; document metrics and lineage; mentor analytics team members and promote best practices.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

Lead enterprise data strategy, architecture, governance, and analytics to create a unified, federated data ecosystem. Build scalable platforms and capabilities, enable self-service BI and advanced analytics, drive AI enablement, steward data governance and quality, and lead change management, talent strategy, and adoption across global teams to support programs, fundraising, and operations.

Design, build, and maintain cloud-native ETL/data pipelines using Databricks and AWS Glue. Ingest and transform structured and semi-structured data with PySpark, Python, and SQL. Integrate sources including MongoDB, implement data quality checks, optimize pipeline performance, and support production deployments, monitoring, and troubleshooting while collaborating with architects and analysts.

Design, build, and operate large-scale batch and streaming ingestion pipelines, medallion/lakehouse layering, identity resolution and golden-record consolidation, data-quality gates and reconciliation, source-to-canonical mappings and crosswalks, schema enforcement, and observability for client onboarding and production data flows.