Top Data & Analytics Jobs in Portland, ME

Leads and develops the Data Engineering team while remaining hands-on with scalable pipelines, ETL/ELT processes, data warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical strategy; improves automation and monitoring; and partners with Finance and business stakeholders to translate data needs into practical solutions.

Lead enterprise Master Data Management strategy, architecture, governance, data quality, and integration initiatives across customer, product, supplier, employee, and financial domains. Design data models, hierarchies, matching and survivorship rules, integration patterns, and implementation roadmaps. Advise clients on Oracle and Salesforce ecosystems, lead workshops and executive presentations, guide solution delivery, mentor consultants, and contribute to proposals and statements of work.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data warehouses supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains SSRS reports, implements data quality controls, migrates legacy systems, and collaborates with business and technical teams. The role also promotes AI-assisted development practices and documents technical procedures.

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.

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.

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.

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.

Supports commercial loan system administration, department reporting, and analysis of deposit and lending activities. Works with large datasets across multiple systems to synthesize actionable insights, collaborates with credit union departments, and performs related administrative and analytical tasks under supervision. The internship is fully remote with optional office work and is limited to 29 hours per week.

Leads end-to-end healthcare payer data conversions between platforms, including profiling, transformation mapping, ETL design, mock and parallel runs, bulk loading, validation, testing, defect resolution, documentation, and client reporting. Develops Python and SQL conversion routines, manages risks and timelines, collaborates with onshore and offshore teams, and mentors junior staff. Requires expertise in claims, enrollment, or provider data and extensive technology integration leadership.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.