Top Remote Data & Analytics Jobs in Portland, ME

Analyze large datasets using SQL and Python, build dashboards and recurring reports, identify trends and data-quality issues, and translate ambiguous business questions into actionable insights. The role partners with product, customer success, and engineering teams, documents metrics and methodologies, and presents findings to technical and non-technical stakeholders. Healthcare data, supply chain analytics, modern analytics engineering, and BI visualization experience are advantageous.

Build and maintain a regulatory reporting data platform using SQL and dbt, including models, automated validations, data pipelines, and testing. Investigate data issues end to end, validate submissions, support audits and legal data requests, monitor upstream changes, collaborate with product, engineering, compliance, and leadership, and participate in on-call coverage. WOOF WOOF

Leads healthcare data architecture, modeling, normalization, transformation, and integration across FHIR, OMOP, STDM, HL7, and related standards. Oversees data pipelines, terminology mapping, deduplication, quality initiatives, interoperability strategies, and technical documentation. Collaborates across technical and non-technical teams to resolve complex data issues and explore AI and machine learning applications. Requires deep expertise in clinical informatics, healthcare data standards, SQL, Python, R, dbt, ETL, cloud platforms, and version control.

Administer and optimize production and non-production databases for a large-scale VA data migration. Responsibilities include installation, patching, performance tuning, backup and recovery, security, AWS database provisioning, data conversion, validation, troubleshooting, high availability, disaster recovery, and technical documentation. The role collaborates with developers and analysts across on-premises and AWS environments while ensuring database availability, integrity, and security.

Analyze marketing and media data to measure TV and streaming campaign effectiveness, validate data and assumptions, identify actionable insights, and create clear visualizations and client-ready presentations. The analyst will use Excel, BI and data platforms, SQL, and AI tools to accelerate analysis while independently validating results and explaining methodologies, KPIs, and recommendations to internal teams and clients.

Own data partner specification negotiations, data mapping, Ascend platform configuration, and internal QA across multiple implementations. Review file specifications, normalize schemas, validate test files, document data dictionaries, and route non-standard requests to Product or Customer Engineering. Coordinate with delivery and engagement teams on UAT, defects, readiness, and capacity constraints. Develop repeatable QA frameworks, implementation playbooks, procedures, and training materials while ensuring configurations meet quality standards before go-live.

Design, build, and own business-critical data infrastructure, including AWS Glue and Spark pipelines, cloud warehouse or lakehouse systems, orchestration, and Tableau BI platforms. Define event-driven contracts, data models, schemas, observability, and batch or streaming architectures across multi-tenant, multi-region environments. Collaborate across producer and consumer teams while owning production data infrastructure and outcomes end to end.

Collect, clean, validate, and analyze operational data from multiple systems to support customer reporting and performance monitoring. Process SQL-based uploads, maintain accurate datasets, identify trends and anomalies, document reporting procedures, and create standardized workflows, dashboards, and tools. The role also supports process improvement and automation initiatives while collaborating with cross-functional teams and traveling to customer sites.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

Leads a team delivering Customer Success reporting, dashboards, KPI frameworks, and operational analytics. Oversees reporting accuracy, data integrity, recurring metrics, workflow improvements, and automation. Partners with Customer Success and services leadership to translate business needs into analytical requirements, prioritize initiatives, standardize reporting methodologies, and communicate performance insights. Coaches analysts, manages workload and development, and supports data-driven operational decision-making and scaling.