Top Senior Data & Analytics Jobs in Portland, ME

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Designs and maintains pipelines migrating financial data from legacy Oracle databases to Oracle ERP. Develops SQL and PL/SQL transformations, backend integrations, data mappings, reconciliation, testing, monitoring, troubleshooting, and documentation. Partners with accounting, business, and technical stakeholders to resolve issues and support large-scale financial transformation. The role also involves AI tool adoption, code reviews, solution design, deployment activities, governance, communication, mentoring, and project oversight.

Leads data strategy and analytics for the Risk/Credit team, translating complex datasets into insights for Credit, Operations, Marketing, Product, and Finance. Responsibilities include automating Python/R workflows, building Tableau dashboards, partnering with Data Engineering on Databricks workflows, conducting strategic analyses, monitoring data quality, presenting recommendations to leadership, and documenting analytical methodologies. The role requires strong SQL, cloud data warehouse, visualization, analytics programming, distributed computing, and version-control expertise.

Own end-to-end data platform architecture across ingestion, lakehouse, warehouse, transformation, modeling, governance, and analytics delivery. Build pipelines, Spark and SQL transformations, data models, and production code. Design Databricks or Microsoft Fabric lakehouses, implement Delta Lake and medallion architecture, establish access controls and governance, and integrate databases, SaaS applications, APIs, files, and legacy systems. Evaluate platform choices based on client workloads, licensing, skills, and cost.

Lead the firm’s analytics strategy, roadmap, and growing analyst team. Partner with executives and law firm operations leaders to define KPIs, evaluate performance, identify bottlenecks, build forecasting and predictive capabilities, and translate data into business actions. Guide SQL, Python/R, statistical modeling, visualization, data quality, and analytics infrastructure standards while partnering with technology and data engineering teams to embed insights into operational workflows.

Owns data operations supporting client reporting, campaign delivery, and month-end billing. Troubleshoots data and backend issues across Salesforce, Fivetran, Snowflake, Sigma, and partner platforms; builds workbooks, views, models, dashboards, and custom analyses; manages permissions, onboarding, data feeds, and external data delivery; resolves campaign, reporting, reconciliation, attribution, and data-quality issues; automates processes and coordinates cross-functional resolution.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Lead the technical direction, architecture, and roadmap for the company’s data platform. Design reliable large-scale pipelines, establish standards for modeling, testing, observability, and orchestration, and mentor data engineers. Partner with engineering, product, analytics, and business stakeholders to align infrastructure investments. Lead incident root-cause analysis, evaluate data technologies, and support scalable analytics, machine learning, and real-time processing capabilities.

Lead the design, development, optimization, and maintenance of scalable batch and streaming data pipelines. Build ETL/ELT workflows using Python, SQL, and AWS services; optimize data models and cloud data platforms; ensure data quality, integrity, reliability, and performance; establish CI/CD and infrastructure-as-code practices; resolve pipeline issues; document systems; and mentor junior engineers.

Own the digital measurement ecosystem across websites, quote engines, customer portals, and account applications. Lead Google Tag Manager tracking frameworks, data-layer standards, Google Analytics 4 configuration, conversion events, attribution, funnel analysis, experimentation measurement, data-quality audits, and analytics governance. Partner with Marketing, Product, Engineering, and Data teams to translate customer behavior into actionable insights that improve growth, conversion, and customer experience.