Top Data & Analytics Jobs in Portland, ME

Collects and analyzes data from project, financial, maintenance, and field systems; builds KPI dashboards and recurring reports; identifies trends, risks, and opportunities; supports budgeting and forecasting; validates data with project and operations teams; and communicates actionable insights to leadership while improving reporting processes.

Own the end-to-end architecture and roadmap for MyFitnessPal’s product data stack. Coordinate data platform contributions across engineering, analytics, and BI teams; establish data movement, standardization, governance, security, and compliance practices; evaluate and consolidate tools; manage access, licensing, and spend; and drive architecture standards, documentation, and adoption across the organization.

Performs data quality assurance for large datasets by validating imports, updates, transformations, and extracts. Writes SQL test queries, reviews database logic, executes test cases, identifies defects, verifies corrections, and documents testing results. Collaborates with data engineers, business analysts, project managers, and QA professionals to ensure data integrity and compliance with requirements.

Designs and supports enterprise data integrations, ETL/ELT pipelines, APIs, data models, and transformations across Microsoft platforms. Maintains data quality, governance, security, and reliability while developing CI/CD pipelines, deployment automation, and release processes. Troubleshoots integration and deployment issues, manages environments, supports reporting and analytics, and collaborates with technical and business stakeholders to deliver scalable data solutions.

Designs and delivers analytics solutions on Snowflake using dbt, SQL, and modern ELT practices. Responsibilities include data modeling, pipeline development, migrations, performance optimization, data quality, security, orchestration, CI/CD, and release management. Leads discovery and requirements gathering, translates business needs into analytics architectures, provides technical guidance, and communicates recommendations to client stakeholders in agile consulting environments.

Build risk data models, analyze fraud and risk trends, create Tableau dashboards and automated reports, and deliver actionable insights to Risk Operations leaders. The role manages analytics requirements and roadmaps while using SQL, dbt, Dataform, and AI tools to support fraud strategy, compliance monitoring, loss forecasting, and executive reporting.

Leads Gartner research and thought leadership on data management, governance, and AI. Develops actionable insights, analyzes market trends, advises executive clients, delivers presentations, supports sales and conferences, reviews content, improves research processes, and mentors analysts. Requires deep expertise in data and analytics, AI implementation, data security, data value creation, executive communication, research, and strategic analysis. Remote U.S. role with travel up to 25%.

Build and maintain IICS ETL/ELT pipelines, develop Amazon Redshift data models, and use SQL and Python for data transformation and automation. Create Power BI reporting models, implement data quality checks, document pipelines, and collaborate with client stakeholders and engineering teams. The role requires autonomous work, strong communication, and the ability to translate technical solutions into business context, with bonus value for Veeva or life sciences experience.

Own data quality across systems by designing validation rules, reconciliation processes, profiling workflows, and quality controls. Investigate discrepancies to identify root causes, apply statistical methods to detect anomalies, evaluate machine learning model outputs, and communicate findings to technical and non-technical stakeholders. Partner with engineering, BI, and business teams to establish data quality standards, metrics, controls, and reporting practices.

Own board-grade analytics deliverables for client engagements by transforming Salesforce pipeline data and BI outputs into executive narratives, presentations, and reusable reporting templates. Analyze pipeline, funnel, and revenue performance; explain variance; write causal commentary; present findings to PE sponsors and executives; and train client teams to maintain deliverables independently.