Top Data & Analytics Jobs in Portland, ME

Lead and develop a team of data engineers while contributing hands-on to design, implement, and operate scalable, secure data pipelines. Partner with product, software, and architecture teams to deliver cloud-native data solutions, ensure production reliability, and drive engineering best practices and continuous improvement.

Build and own Snowflake data models and dbt pipelines for a Customer Data Platform supporting marketing segmentation and customer journeys. Integrate complex source systems, manage data architecture and governance, conduct data discovery, and translate business rules into scalable solutions. Collaborate with engineering, analytics, product, and business stakeholders; lead technical workshops; mentor junior engineers; and implement automated data quality testing, CI/CD workflows, and AI-augmented engineering practices.

Produce weekly newsletter and quarterly magazine content focused on shipping finance; maintain and analyze proprietary deal and registry databases; create charts, short-form digital analysis, and support editorial production and conference programming.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Short-term, paid project to export and submit personal financial data from multiple apps. Participants must provide exports from specific financial categories, create two financial artifacts, redact PII on-device using a provided tool, and follow quality and timeline requirements until Final Approval.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

Analyze large, multi-source datasets to identify trends, risks, opportunities, and performance drivers. Build Power BI dashboards, reports, KPI scorecards, and analytics-ready datasets using SQL. Partner with business leaders and stakeholders to clarify questions, evaluate initiatives, maintain data quality, and develop actionable recommendations. Present insights through clear visualizations and data storytelling, while managing multiple analytics assignments and contributing to team best practices.

Designs enterprise-scale federated data architectures integrating Palantir Foundry and Databricks. Establishes data-sharing, governance, lineage, metadata, and ownership standards across cloud platforms. Leads legacy ETL modernization, builds scalable PySpark and Spark SQL pipelines, architects secure connectivity, and enables AI, machine learning, real-time analytics, and self-service data products. Serves as the technical authority, leads architecture reviews, and manages stakeholder relationships.

The Senior Data Analyst gathers business and data requirements, assesses data quality and sources, maintains data dictionaries and lineage, defines KPIs, validates reports, and delivers actionable insights. The role supports data governance, PII protection, integrations, data transformation, platform performance, business continuity, and process improvement. It collaborates with stakeholders and data teams, mentors colleagues, resolves complex issues, and contributes to enterprise data strategy and technology decisions.