Top Analysis Reporting Jobs in Portland, ME

Supports IAA’s vehicle enhancement and make-ready program through recurring dashboards, baseline metrics, vendor and cost analysis, workflow documentation, process improvement, and leadership reporting. Tracks volume, cycle time, aging, completion, exceptions, SLA performance, utilization, and cost variances to identify operational risks and recommend improvements that support efficiency, margin, seller experience, and GTV growth.

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and documentation. Ensure data quality, consistency, and trusted marketing metrics across sources. Partner with data engineering and BI teams on deployments and production issues, investigate model discrepancies, perform analytical work, mentor apprentices, and contribute to applied AI initiatives.

Analyzes healthcare data using SAS, SQL, Excel, R, Python, Tableau, and related tools to identify trends, variances, outliers, and improvement opportunities. Creates reports, dashboards, tables, and visual presentations for technical and non-technical stakeholders. Supports healthcare intervention measurement, data entry, database functions, documentation, research, and policy compliance.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.

Lead the architecture and implementation of enterprise-scale GCP data ecosystems, including PB-scale platforms and AWS-to-GCP migrations. Establish data platform roadmaps, self-service architectures, governance, security, quality, and Infrastructure as Code standards. Architect solutions using GCP data, analytics, AI, streaming, and containerization technologies. Lead cross-functional engineering teams, mentor staff, advise executives, manage technical risks, and communicate the business value of data initiatives.

Designs, implements, operates, and optimizes database and data-management systems supporting classified intelligence missions. Responsibilities include data architecture, SQL and NoSQL database administration, query and throughput optimization, ETL pipeline optimization, performance monitoring, capacity planning, documentation, backup and restore, audit logging, and continuity planning. The role requires onsite work in the Washington, DC region, U.S. citizenship, and a final Top Secret clearance with SCI eligibility or access.

Build and maintain scalable data solutions in Palantir Foundry, including ontologies, data assets, pipelines, workflows, analytics, and AI/ML applications. Translate stakeholder requirements into governed, secure, and maintainable solutions; troubleshoot platform issues; support data governance and cloud modernization; and create documentation, training, and user support resources for FAA platform users.

Design, build, and maintain operational dashboards and SQL-based data models. Translate stakeholder questions into queries, validate data quality, migrate reporting to Databricks/dbt, document analytics, and support metric governance to deliver trusted operational insights.

Supports Tier 1 help desk requests, reporting, data validation, documentation, testing, and process improvements. The role assists with automations, maintains Teams knowledge content, and collaborates with senior analysts and developers while learning Power BI, Power Apps, Azure, and ARES capabilities. Responsibilities include identifying workflow inefficiencies, following up on assigned tasks, escalating issues, and supporting the transition from BusinessObjects to ARES.

Own high-impact data products, ingestion pipelines, and granular data feeds end to end. Analyze massive datasets to answer client and stakeholder questions, ensure data accuracy, and derive actionable insights. Improve products and pipelines by developing methodologies, scaling third-party datasets, and creating data modules. The role requires clear communication, attention to detail, methodical problem solving, prioritization, experimentation, and strong ownership. Training includes SQL, PySpark, Python fundamentals, and internal tools.