Top Remote Data & Analytics Jobs in Baltimore, MD

Supports information collection and organization for manuals, training materials, installation guides, proposals, and reports. Edits technical and customer-facing documentation, assists with financial and administrative tasks, and maintains systems, operations, and procedural documentation libraries. Collaborates remotely with project managers, requirements analysts, Scrum Masters, developers, testers, and government stakeholders in Agile ceremonies while complying with CUI and cybersecurity requirements.

Manage Epic Cogito reporting projects for healthcare organizations by gathering requirements, designing and developing reports, performing testing and validation, and providing post-implementation support. Collaborate with internal and external stakeholders, solve complex problems, and manage multiple projects independently while delivering healthcare IT and analytics solutions.

The Director of Biostatistics leads statistical strategy and analysis for clinical trials in oncology, collaborating with clinical teams and regulatory bodies.

Supports the Product Innovation Team by modifying and optimizing SQL queries, visualizing data with BI tools, identifying insights, and analyzing enhancements. Participates in coding, testing, documentation, and discussions while providing customer service and managing multiple tasks. The internship requires at least 30 hours per week and emphasizes advanced Excel and visualization skills, plus familiarity with Python and SQL.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.

Transforms complex retail and consumer data into business-ready datasets, scalable pipelines, reporting solutions, automation, and client deliverables. Designs data integration solutions in Unify, troubleshoots connector issues, explores AI-driven improvements, and partners with Product, Engineering, Client Development, Operations, and business stakeholders to define requirements and deliver scalable solutions.

Designs, develops, maintains, and improves software applications and business processes. Responsibilities include full lifecycle development, troubleshooting, performance and reliability improvements, CI/CD collaboration, requirements analysis, technology research, package maintenance, portfolio planning, and mentoring junior engineers. The role requires independent work, cross-functional collaboration, and strong software development, documentation, and problem-solving skills.

Build and maintain scalable Python-based data pipelines and cloud-native solutions supporting U.S. Army financial-system modernization. Develop ETL/ELT workflows using AWS Glue, Lambda, S3, and CloudWatch; integrate APIs and diverse file formats; improve data reliability, performance, security, and governance; implement testing and monitoring; and collaborate with engineers and stakeholders in an Agile environment.

Lead marketing analytics across channel, campaign, cohort, and funnel performance. Build dashboards, reporting, AI-enabled analytical workflows, and data models that support marketing decisions. Conduct ad hoc analyses, evaluate attribution and incrementality, monitor data quality, and translate complex findings into executive recommendations. Partner with marketing and marketing science teams, influence strategic decisions, establish analytical standards, and mentor analysts.