Top Entry Level Data & Analytics Jobs in Portland, ME

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Designs, implements, and optimizes Salesforce Data Cloud solutions. Responsibilities include configuring data streams, integrating Salesforce and legacy data with MuleSoft or ETL tools, mapping data objects, creating identity resolution rules, building calculated insights with SQL, developing customer segments, configuring activation targets, and managing data governance, privacy compliance, system performance, storage, and usage.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Serves as the technical and client-facing link between Inca Digital’s data engine and high-stakes financial and government clients. Conducts OSINT, web scraping, enhanced due diligence, blockchain and crypto investigations; analyzes large datasets; audits and synthesizes technical findings into intelligence reports; briefs executive stakeholders; and coordinates cross-functional teams to align products with client needs.

Leads client-facing database migration and modernization engagements across AWS, relational, and NoSQL platforms. Designs migration architectures, data pipelines, ETL/ELT workflows, proof-of-concepts, and reusable technical accelerators. Facilitates executive conversations and workshops, translates complex technical solutions into business outcomes, and supports pre-sales opportunities. The role also develops GenAI-enabled migration tooling, influences senior stakeholders, and collaborates with distributed engineering, product, and data teams.

Develop and evaluate realistic Excel tasks and solutions for AI training data. Apply advanced Excel capabilities including formulas, PivotTables, data modeling, dashboards, Power Query, Power Pivot, and VBA across multiple industries and business functions. Provide written feedback, close research knowledge gaps, improve model performance, and collaborate with subject matter experts. Requires reliable weekday availability, initially 40 hours per week and 20 hours thereafter.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Manage MSSQL and PostgreSQL databases, including installation, configuration, monitoring, performance tuning, security, backup and recovery. Develop optimized SQL queries, stored procedures, triggers, and views. Use AWS services for database deployment and scaling, Git/GitHub for version control, and collaborate with developers and system administrators. Support data warehouse design, ETL processes, reporting requirements, and database troubleshooting.