Top Senior Data & Analytics Jobs in Portland, ME

Own measurement strategies for large omnichannel brands, including test design, marketing mix modeling, incrementality analysis, attribution interpretation, and budget recommendations. Translate complex findings into actionable guidance for executives and marketing teams, support prospect conversations, identify customer and product gaps, and collaborate with Product, Data Science, and Customer Success. The role requires strong paid media expertise, omnichannel fluency, statistical measurement literacy, and the ability to communicate uncertainty and recommendations clearly.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Lead enterprise adoption and toolchain integration for business analysis across federated teams. Design enablement curriculum, integrate standards and AI-enabled tooling (Azure DevOps, ServiceNow, Nexus, Power BI), build decision-oriented dashboards and leading indicators, drive adoption and governance, surface portfolio trends, and partner with stakeholders to improve delivery predictability and compliance.

Leads marketing experimentation, conversion optimization testing, incrementality analysis, marketing mix modeling, and statistical measurement programs. Designs rigorous experiments, manages SQL-based testing databases, validates data, develops executive reporting, and translates complex findings into investment and marketing recommendations. Partners with marketing, product, technology, analytics, and executive teams while potentially mentoring junior analysts.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Analyzes platform usage, client growth, query performance, infrastructure capacity, and cloud/AI costs. Builds scaling scenarios, monitors AWS and GCP utilization, identifies anomalies and waste, owns product analytics reporting, and partners with Engineering and Product on database scaling decisions. The role also documents optimization results, capacity thresholds, vendor utilization, and model assumptions while translating technical findings for business stakeholders.

The Sr. Data Modeler designs and implements data architectures, optimizes SQL queries, and develops data pipelines to support analytics initiatives ensuring scalability and alignment with business needs.

Leads web analytics strategy, GA4 and GTM implementations, tagging specifications, dashboards, reporting, experimentation, and data-quality monitoring. Analyzes user behavior, conversion funnels, feature adoption, and A/B test results to provide actionable recommendations for product and business decisions. Partners with product, engineering, UX, and business stakeholders and communicates analytical findings to technical and non-technical audiences.

Own marketing channel performance analysis, reporting, experimentation, dashboards, and measurement frameworks. Develop incrementality, LTV, and forecasting analyses; identify data quality risks; and translate complex findings into actionable recommendations for Marketing, Growth, and Finance stakeholders. The role requires strong SQL, BI reporting, marketing measurement, experimentation, and analytical communication skills, with AI tools used to accelerate analytics workflows.