Top Senior Data & Analytics Jobs in Baltimore, MD

Own and build Underdog’s regulatory reporting data platform, including dbt models, resilient pipelines, automated validations, monitoring, and reporting roadmaps. Partner with regulators, engineering, product, compliance, and leadership; lead audits, investigations, root-cause fixes, data governance, and on-call operations. WOOF WOOF.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

Leads internal data analysis projects, identifies business opportunities and risks, delivers strategic insights to leadership, manages junior analysts, develops dashboards, and improves data quality across company platforms. The role requires translating quantitative findings for nontechnical audiences and applying healthcare or pharmaceutical dataset experience to strategic decision-making.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Analyze and design enterprise healthcare data solutions using Snowflake and advanced SQL. Perform data profiling, reconciliation, anomaly detection, source-to-target validation, and quality testing across claims, membership, provider, and health plan data. Support data architecture, dimensional modeling, governance, metadata, lineage, cataloging, and MDM initiatives. Prepare data for AI and advanced analytics, translate business requirements into solutions, resolve data issues with stakeholders, and communicate actionable insights while following security and HIPAA requirements.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Leads and scales a global data engineering and analytics team while remaining hands-on with architecture, pipelines, data quality, dimensional modeling, dashboards, reporting, and customer-facing deliverables. Owns the analytical data platform supporting clinical, operational, product, financial, and customer data. Partners with cross-functional stakeholders, investigates complex data issues, improves platform reliability and security, and mentors engineers.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Own enterprise analytics audits, implementations, data quality, attribution, segmentation, SQL analysis, BI dashboards, experimentation measurement, and client reporting. Serve as the senior analytics partner for major accounts, present insights to executives, train client and internal teams, and help define analytics and experimentation services. The role requires hands-on Adobe expertise, autonomous prioritization, technical collaboration, and the ability to connect organic and AI search performance to pipeline and revenue.