Top Analysis Reporting Jobs in Providence, RI

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

The job description provides no substantive responsibilities or qualifications beyond the Director, Data Engineering title. The posting contains general company information, benefits, equal employment opportunity language, and application details. Specific duties, required technologies, experience, education, and other role expectations are marked as TBD or not provided.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

Lead current-year and long-term AP volume forecasting using statistical and AI/ML methods; diagnose supply-demand drivers; close analytic data gaps; translate analyses into strategic recommendations; mentor staff and communicate forecasts and risks to senior stakeholders across College Board.

Manage and mentor a data engineering team while leading Snowflake-based platform architecture, enterprise data integrations, governance, observability, and pipeline reliability. Partner with analysts, business leaders, product management, and engineering teams to improve trusted self-service data access. Establish engineering standards, modernize the data stack, optimize costs, and deliver an AI-ready data foundation supporting analytics, reporting, and future AI initiatives.

Validate and triage clinical data across HL7, FHIR, and Databricks pipelines using SQL. Trace transformations end-to-end, identify and remediate data quality issues, partner with engineering/product/clinical teams, document findings, and support client go-lives to ensure clinically accurate, production-ready data.

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.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Support a core set of clients by guiding users on Numerator's Insights platform, performing ad-hoc/custom analyses using proprietary consumer panel data, delivering actionable analytic insights and recommendations, and presenting findings across channels including industry forums and written content.

Design and maintain a formal analytics data layer (AWS Glue, Athena/Presto, LookML); build data models, pipelines, and automated quality checks; investigate anomalies; document metrics and lineage; mentor analytics team members and promote best practices.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.