Top Data & Analytics Jobs in Baltimore, MD

Design, build, and modernize data solutions in a hybrid cloud environment. Implement ingestion, ELT pipelines, APIs/microservices, streaming and batch processing, data security, unit tests, code reviews, and performance optimizations to support analytics and applications.

Designs, develops, and maintains cloud and on‑prem data solutions for provider master data, roster integrations, contract/network participation tracking, ETL/ELT pipelines, reporting datasets, and dashboards. Provides production support, documentation, data modeling, and stakeholder collaboration to deliver validated analytics and BI products for network management and provider operations.

Partner with channel leadership to own performance reporting, dashboards, and scorecards; analyze KPIs, identify risks and opportunities, deliver data-driven insights, support forecasting, target-setting, and compensation measurement, and drive continuous improvement and reporting automation across the assigned sales channel.

Builds and manages ETL processes and data pipelines in Azure Databricks using PySpark. Integrates data from multiple sources, tunes Spark and SQL for performance, troubleshoots pipeline failures, automates manual processes, and supports reporting and analytics to inform business decisions.

Serve as the technical bridge between GenLogs' data platform and customers: discover high-value use cases, communicate capabilities, build rapid prototypes/POCs (SQL, Snowflake, Python/Streamlit), translate requirements, and guide engineering/data teams to production while traveling to customers ~1-2 times/month.

Serve as embedded BI lead for a divisional sales book, delivering actionable insights from syndicated and commercial data, leading opportunity analysis, training field teams, supporting Power BI reporting, and translating complex data into executive presentations to drive sales performance and planning.

Lead end-to-end category insights for strategic account chains using syndicated, retailer POS, and internal data. Develop analyses, reporting, and recommendations to drive sales and profit, partner with sales and BI, support category reviews and business planning, and present insights to stakeholders.

Serve as embedded BI partner for an assigned division, delivering actionable commercial insights from syndicated and internal data, leading opportunity analyses, training field teams, supporting Power BI reporting, and translating complex data into executive presentations to drive sales performance and program ROI.

Collaborate with business teams to define analytics requirements, explore and model operational and financial data, produce forecasts and recommendations, and build dashboards and visualizations to support decision-making. Contribute to data stewardship, metadata, data quality remediation, and liaison with enterprise analytics for data model needs.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Design, build, test, deploy, and support enterprise data solutions for financial reporting, reserving, and claims. Develop and maintain pipelines, transformations, data models, and controls using SQL, Python, PySpark, and Microsoft Fabric. Automate reconciliations and data quality checks, partner with business stakeholders, perform testing, review code, mentor peers, and resolve production issues to improve reporting accuracy and operational efficiency.

Lead process mapping, analysis, and redesign to improve efficiency and outcomes. Partner with stakeholders to gather requirements, analyze data (SQL, Excel, Power BI), define KPIs, apply Lean/Six Sigma methods, and support automation and digital transformation initiatives to drive growth, profitability, and loss reduction.

Lead Business Analyst partners with leadership to analyze data, evaluate processes, define requirements, build reports/dashboards, drive cross-functional improvements, and deliver executive-ready insights and recommendations to improve efficiency and business outcomes.

Lead and own end-to-end delivery of enterprise Data & AI programs, define data strategy and architecture (lakehouse/warehouse), drive modernization, governance, data quality, metadata/lineage, and AI adoption. Manage client relationships, account growth, program budgets, delivery teams, and stakeholder communications to ensure on-time, on-budget outcomes.