Top Senior Analytics Jobs in Baltimore, MD

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

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.

Leads a team delivering Customer Success reporting, dashboards, KPI frameworks, and operational analytics. Oversees reporting accuracy, data integrity, recurring metrics, workflow improvements, and automation. Partners with Customer Success and services leadership to translate business needs into analytical requirements, prioritize initiatives, standardize reporting methodologies, and communicate performance insights. Coaches analysts, manages workload and development, and supports data-driven operational decision-making and scaling.

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.

Own Chamber’s central analytics and reporting function for value-based cardiology contracts and clinical programs. Build SQL models, governed metric definitions, a semantic layer, data-quality standards, executive and board reporting, payer deliverables, SLA tracking, and clinical analytics foundations. Establish reporting governance so finance, clinical, and operations teams can self-serve trusted insights. This hands-on player-coach role requires healthcare claims and clinical data expertise, value-based care fluency, and the ability to build a reporting function from scratch.

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.

The Senior Analyst will contribute to data enablement, enhance data access, develop strategic data sets, and support analytics solutions while collaborating with stakeholders to improve overall data management and governance.

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.

Lead finance-focused data and analytics initiatives: build scalable data models and Power BI dashboards, develop a Finance AI roadmap, analyze margin/utilization drivers, support profitability and pricing strategies, and deliver executive-ready insights partnering with FP&A, Operations, IT and senior leadership.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

The Senior Analytics Engineering Manager will oversee analytics engineering vision, manage a team, ensure data quality, and drive data-driven decisions across departments.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Lead design, development, and migration of enterprise SAP BW solutions to SAP Datasphere; architect hybrid data flows with Microsoft Fabric and Azure Synapse; integrate BW with Salesforce and SQL Server; embed AI (Copilot, Azure OpenAI) into analytics; manage deliverables, mentor offshore teams, enforce standards, and ensure data integrity and performance for reporting and analytics.

Execute underwriting and case-valuation analytics: write and optimize SQL, build dashboards, maintain valuation models, forecast case outcomes, monitor portfolio performance, and translate insights for operational and marketing stakeholders.

The Senior Analytics Engineer will design and build scalable data pipelines using dbt and Snowflake, providing technical guidance, conducting audits, and mentoring junior engineers while leveraging AI tools to enhance productivity.

Lead Oracle Cloud reporting initiatives, ensuring data accuracy and performance while mentoring junior analysts and collaborating with stakeholders.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Leads and develops an analytics engineering team that builds data pipelines, analytical models, reporting, and scalable data infrastructure. Partners with Product, Engineering, Growth, and Operations to deliver reliable, actionable data. Establishes governance, quality, testing, documentation, and modeling standards while prioritizing analytics projects and communicating with stakeholders.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.