Top Data & Analytics Jobs in Baltimore, MD

Lead design and implementation of scalable GCP-based data pipelines and semantic layers for enterprise healthcare analytics. Drive ingestion automation, BigQuery optimization, ETL/ELT orchestration, observability, data quality, and metadata management. Provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to deliver production-ready cloud data solutions.

Lead data analysis and architecture efforts for healthcare insurance projects: gather requirements, design source-to-target mappings and data models, enforce enterprise data standards, support data profiling/conversion, and participate in solution planning and estimations. Mentor/train others and translate business drivers into technical requirements across the SDLC.

Perform systems planning analyses, evaluate and model business and system requirements, analyze and document complex business problems, develop and approve test plans, and coordinate moderate-scope upgrades and rollouts to support leadership decision-making and infrastructure.

Design and guide implementation of a Data Lake solution using Exadata and Oracle Data Integrator. Collaborate with functional and data teams, assess requirements, produce design documents, recommend architecture/process improvements, manage build phase, and ensure code quality. Requires strong relational database knowledge and communication skills.

The Senior Data Conversion Lead will transform complex legacy data for retirement benefits, ensuring client data integrations and championing process innovations.

The Analyst will deploy, maintain, and troubleshoot business applications, manage system setups, define testing procedures, translate technical specs, and provide documentation.

The Target Analyst Reporter 3 prepares intelligence reports using passive and active data collection methods, collaborates with analysts, and maintains quality control on reports for government clients.

Implement and support Informatica ETL solutions, tune mappings and SQL for high-volume data warehouses, handle production issues, build auditing/error-handling frameworks, prepare technical documentation, and collaborate with QA and global teams to define and improve development/support processes.

Design, build, and maintain scalable cloud-native data platforms and pipelines (batch, streaming, near-real-time). Implement distributed processing (Spark), event-driven systems (Kafka), governance, security, observability, and support ML/analytics. Partner with cross-functional teams, optimize performance/cost, and mentor engineers while enabling production-grade data products.

Define and improve data quality processes, analyze data issues, collaborate with stakeholders to document requirements, monitor data quality metrics, and support remediation efforts to ensure accurate, reliable data.