Top Senior Data & Analytics Jobs in Portland, ME

Lead global Customer Success analytics strategy, architect data and health/adoption models, oversee data pipelines, deliver executive-level storytelling, mentor an analytics team, and drive cross-functional KPI alignment and self-serve BI adoption.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, and operate scalable data pipelines and integrations for an RCT-driven voter platform. Extend data platform, optimize cost and reliability for election-scale loads, lead cross-system projects, ensure data quality, document systems, and participate in on-call incident response.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Designs, implements, and supports enterprise data warehouses, data marts, operational data stores, ETL frameworks, and data integration solutions. Responsibilities include source analysis, data profiling, pipeline development, data mapping, transformation design, architecture documentation, testing, quality validation, deployment coordination, performance optimization, and regulatory compliance. The role collaborates with architects, business users, operations teams, and cross-functional partners while using Talend and cloud, big data, database, and scheduling technologies.