Top Analysis Reporting Jobs in Portland, ME

Designs and governs PANTHERx’s semantic layer on Databricks, including Unity Catalog metric views, Gold-layer tables, promotion logic, and reusable data products. Establishes canonical, versioned business metrics for BI, analytics, partner feeds, and AI inputs. Responsibilities include data modeling, migration, validation, observability, documentation, governance, stakeholder collaboration, and mentoring analytics engineers. The role requires advanced SQL, Python, and PySpark skills, strong Databricks expertise, and experience operating across analytics engineering and data engineering.

Lead the Ads Signals & Measurement data engineering team across conversion and attribution, measurement foundations, audience onboarding, and identity. Set cross-organizational technical direction, resolve scalable data architecture decisions, mentor engineers, and partner on platform-wide initiatives. The role requires expertise in modern data stacks, advertising technology data flows, privacy and governance, distributed systems, and emerging AI technologies.

Own trade performance reporting, dashboards, scorecards, promotional ROI analysis, range review support, category insights, and sales projections for major retail customers. The role turns sales and shopper data into commercial recommendations, supports retailer presentations and new product launches, and collaborates with Sales, Marketing, Supply Chain, Demand Planning, and Finance. It requires strong Excel and presentation skills, commercial curiosity, attention to detail, clear communication, and the ability to manage multiple priorities.

Organize and curate large volumes of raw information: extract key points from transcripts, maintain clean spreadsheets, curate links/resources, tag and categorize content, and follow or propose systems for information structure. Part-time remote role with initial 10 hours/week and potential to grow.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Own Fi’s data platform and analytics roadmap, building event-driven pipelines, warehouse models, self-serve BI, and experimentation capabilities. Lead and develop a team of four analysts while partnering with Finance, Product, and Hardware on forecasting, unit economics, device analytics, retention, and board reporting. Serve as a strategic data leader in a fast-growing Series B startup.

Own and scale client-facing data transformation pipelines for financial and operational data. Build reliable analytical models, data integrity tests, and integrations across diverse sources. Partner with Customer Success and Engineering to troubleshoot data issues, optimize critical pipelines, establish engineering standards, and improve developer experience. Use dbt, SQL, Python, BigQuery, and AI-enabled workflows to deliver performant, well-documented FP&A data products while mentoring teammates and contributing to hiring and onboarding.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design, develop, and optimize enterprise-scale data platforms, pipelines, data lakes, warehouses, and analytical datasets on Azure and Snowflake. Build ETL/ELT processes using ADF, Databricks, PySpark, SQL, Python, and Snowflake. Manage data models, governance, security, quality, CI/CD, cloud migrations, and performance optimization. Collaborate with stakeholders, troubleshoot production issues, document solutions, and support Agile delivery.

Design and govern end-to-end data architecture for an integrated supply-chain e-commerce platform: create data models, build scalable ETL/ELT pipelines, ensure GDPR-compliant data governance, enable real-time analytics, optimize databases, plan retention/archival, and prepare data for LLM fine-tuning and RAG use cases.