Top Senior Analysis Reporting Jobs in Portland, ME

Lead a data engineering team building financial analytic datasets: design and deliver ETL/ELT pipelines, optimize performance, enforce data quality and governance, document workflows, mentor engineers, and architect cloud data infrastructure (GCP/AWS/Azure, BigQuery).

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Develop and maintain operational and research datasets, translate requirements into SQL queries, build reports and data marts, enforce data access/security, document dataset construction and SOPs, and lead data quality and optimization initiatives across research support functions.

Design, build, and enhance executive Power BI dashboards and scalable data models. Develop DAX, Power Query, optimized visuals, KPIs, templates, and governance while iterating with stakeholders to deliver role-based reporting and drill-through navigation.

Restructure and maintain Airtable relational architecture for fertility industry data, implement automations and integrations (Zapier, Softr), enforce data governance and permissions, build dashboards and reports, execute data acquisition and enrichment to scale the database, conduct audits, document SOPs, and support onboarding and system monitoring.

Lead end-to-end SAP S/4HANA data migration: define strategy, analyze and cleanse legacy data, map and transform data, execute mock and production loads using SAP migration tools, validate and reconcile results, support cutover, testing and post-go-live hypercare while partnering with stakeholders and technical teams.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

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.

Collect and analyze competitive intelligence and conduct due diligence and investigations on investment candidates and related entities. Design data collection methods, use research, network analysis and AI/automation tools to produce high-quality reports and actionable recommendations for senior stakeholders, and improve workflow productivity.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.