Top Analysis Reporting Jobs in Baltimore, MD

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

Designs enterprise-scale federated data architectures integrating Palantir Foundry and Databricks. Establishes data-sharing, governance, lineage, metadata, and ownership standards across cloud platforms. Leads legacy ETL modernization, builds scalable PySpark and Spark SQL pipelines, architects secure connectivity, and enables AI, machine learning, real-time analytics, and self-service data products. Serves as the technical authority, leads architecture reviews, and manages stakeholder relationships.

Supports commercial loan system administration, department reporting, and analysis of deposit and lending activities. Works with large datasets across multiple systems to synthesize actionable insights, collaborates with credit union departments, and performs related administrative and analytical tasks under supervision. The internship is fully remote with optional office work and is limited to 29 hours per week.

Leads end-to-end healthcare payer data conversions between platforms, including profiling, transformation mapping, ETL design, mock and parallel runs, bulk loading, validation, testing, defect resolution, documentation, and client reporting. Develops Python and SQL conversion routines, manages risks and timelines, collaborates with onshore and offshore teams, and mentors junior staff. Requires expertise in claims, enrollment, or provider data and extensive technology integration leadership.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Design, build, and operate scalable cloud-native data pipelines and data lake/fabric architectures for Revenue Cycle Management. Implement ETL/ELT, integrate structured and unstructured sources and APIs, ensure data quality, lineage, and governance, and support AI/ML and analytics workloads with CI/CD and DevOps practices.

Lead BI reporting and advanced analytics to optimize marketing campaigns and ROI. Build dashboards, manage multi-source data, ensure data governance, provide actionable insights, mentor juniors, and partner with cross-functional teams to guide strategy.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.