Top Senior Data & Analytics Jobs in Baltimore, MD

The Workday Reporting and Security Analyst develops and maintains Workday reports, dashboards, calculated fields, and People Analytics to deliver workforce insights. The role gathers reporting requirements, validates data, troubleshoots issues, conducts release testing, and documents solutions. It also administers Workday security roles and policies, audits access, enforces privacy and SOX controls, supports internal and external audits, and partners with HR, Finance, IT, vendors, and business stakeholders.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Own Hercules’ full data stack, including ETL from third-party providers into BigQuery, data modeling with dbt and medallion architecture, semantic-layer metric definition, and self-service business intelligence in Omni. Build reliable, maintainable systems and dashboards while applying strong software architecture, API design, code quality, and AI-assisted development practices.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Develop and optimize SQL Server and SSIS billing pipelines, automate data movement and validation with Python, and use R and Python for analytics, anomaly detection, and billing decision models. Build data-quality frameworks, support internal reporting tools, integrate ERP APIs, and collaborate with finance and operations stakeholders. The role requires maintainable, tested, auditable solutions and ownership of features across databases, pipelines, analytics, and internal applications.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Analyze qualitative and quantitative customer feedback, behavioral data, and operational metrics to identify user pain points and root causes. Develop dashboards, KPIs, reporting, and measurement frameworks; quantify experience impacts; and provide evidence-based recommendations. Partner cross-functionally with Product, Engineering, Marketing, Data, and Support leaders to influence prioritization, evaluate initiatives, and improve customer satisfaction, usability, loyalty, adoption, and support demand.

Leads enterprise data architecture for Customer 360, CDP, identity resolution, marketing, personalization, real-time activation, paid media, measurement, and AI capabilities. Designs unified data models, streaming and batch integrations, governed activation patterns, data contracts, APIs, observability, and security controls. Partners with business and technical leaders on roadmaps, architecture reviews, vendor evaluations, and implementation decisions while mentoring engineering teams and ensuring privacy, compliance, and responsible AI standards.