This page tracks remote data engineer openings that are location-eligible for Rhode Island.
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• Own one or more data engineering workstreams, from technical design through production deployment and handoff. • Design and implement scalable data models and lakehouse architectures, including medallion patterns where appropriate. • Optimize performance and cost across Databricks and the underlying cloud: you treat compute spend as your problem, not someone else's. • Orchestrate workflows using Databricks Workflows, Delta Live Tables, or equivalent tooling. • Implement governance, security, observability, and lineage with Unity Catalog, and stand up CI/CD for data. • Design and build AI-ready data platforms that enable reliable analytics, machine learning, and agentic applications. • Work directly with client stakeholders to gather requirements and translate them into technical solutions. • Review code, mentor junior engineers, and maintain high engineering standards across your workstreams. • Contribute reusable accelerators, frameworks, and best practices back to the practice.
• Own one or more data engineering workstreams, from technical design through production deployment and handoff. • Design and implement scalable data models and lakehouse architectures, including medallion patterns where appropriate. • Optimize performance and cost across Databricks and the underlying cloud: you treat compute spend as your problem, not someone else's. • Orchestrate workflows using Databricks Workflows, Delta Live Tables, or equivalent tooling. • Implement governance, security, observability, and lineage with Unity Catalog, and stand up CI/CD for data. • Design and build AI-ready data platforms that enable reliable analytics, machine learning, and agentic applications. • Work directly with client stakeholders to gather requirements and translate them into technical solutions. • Review code, mentor junior engineers, and maintain high engineering standards across your workstreams. • Contribute reusable accelerators, frameworks, and best practices back to the practice.
Role Description International Highlight is growing fast, and with that growth comes data — a lot of it. We’re looking for a focused, organized individual to assist with gathering, logging, and maintaining engagement and performance information across various platforms. You’ll work closely with our Data Manager to ensure accuracy, timeliness, and clear communication between teams. This role is great for those who are detail-oriented and eager to learn how real brand data flows behind the scenes. - Track and input daily engagement metrics using provided templates. - Maintain clean and consistent data records for active campaigns. - Identify inconsistencies and report them to the Data Manager. - Collaborate with other support roles to ensure deliverables are met on schedule. Qualifications - Detail-oriented. - Eager to learn about brand data. Requirements - Ability to work closely with the Data Manager. - Strong organizational skills. Benefits - Flexible remote schedule with consistent weekly goals. - Experience working directly with brand performance systems. - A supportive team focused on growth, clarity, and execution.
• Design multimodal, multitask datasets that teach world models new capabilities — deciding what data to collect, generate, or curate and measuring its effect on model behavior • Run controlled training experiments to understand how data composition drives model performance across tasks and domains • Build and operate large-scale pipelines for synthetic data generation, filtering, and quality control • Define evaluations and benchmarks that measure whether our models are actually improving at the things that matter • Partner with product and creative teams to translate target behaviors and capabilities into concrete data strategies
The Enterprise Platform for Safe and Scalable AI
• Own the cross-platform reference architecture for the Modern Data Platform across Databricks, Snowflake, and Fabric — and evolve it as platforms and partners change • Serve as the engagement-embedded tech lead on the active engagement: architecture, design reviews, and hands-on build on the hardest parts • Lead technical discovery with prospective health system partners — assess source environments (Epic Clarity/Caboodle, Cogito on Cloud, Bulk FHIR, flat-file SFTP) and design a defensible target state • Lead and develop a team of platform-specialist Senior Data Engineers and a Cloud/Platform Engineer; set the technical bar • Review and approve all engineering deliverables — IaC, ingestion patterns, data sharing topology, security controls • Partner with the Engagement Manager and Field CDO to scope SOWs, defend timelines, and surface technical risk before it becomes schedule risk • Coordinate technical handoffs to the QH Platform team via Delta Sharing / Reader Account so AI workflows can be deployed against the modernized foundation • Maintain the technical engagement playbook and a library of accelerators so each engagement starts further along than the last • Carry the technical narrative in pre-sales: present to CTOs, CDOs, and enterprise architects; convert technical credibility into signed work
The Enterprise Platform for Safe and Scalable AI
• Work forward-deployed inside the customer's environment • Own platform-specific architecture and build for your stack during active engagements • Design and implement ingestion from EHR and source systems (Epic Clarity / Caboodle, FHIR, ERP, scheduling, claims) into a medallion lakehouse • Build and harden change-data-capture, transformation, and orchestration pipelines that meet engagement timelines • Configure governance, access control, and the data-sharing pattern that hands clean, AI-ready data to QH's platform • Sustain production environments handed off from prior engagements • Support pre-sales technical discovery and source-data assessment • Ensure every environment meets handoff criteria for the Client Integration team • Cross-train on the other two platforms to keep the team flexible across single- and multi-engagement states
• Lead Data Engineer- Healthcare Interoperability • We are seeking an experienced Lead Data Engineer to join our healthcare technology team with a primary focus on healthcare interoperability. • This role will be instrumental in designing, building, and maintaining our Azure-based data infrastructure, including databases, data pipelines, analytics platforms, and business intelligence solutions — with an emphasis on integrating with Electronic Health Record (EHR) systems and healthcare data exchange standards (FHIR, C-CDA, HL7). • The ideal candidate will have deep expertise in the Microsoft data stack, strong architectural thinking, and experience with healthcare data compliance requirements. • This senior-level position will also provide technical leadership, mentorship to the broader engineering team and reports to the Director of Data Engineering. • Design and implement scalable, secure data architectures on Azure to support business intelligence, analytics, and operational systems • Build and maintain data pipelines using Azure Data Factory, Azure Synapse and related services • Contribute to database designs across multiple platforms (Azure SQL Database, SQL Managed Instance, PostgreSQL, CosmosDB) • Optimize data storage strategies across Azure Blob Storage, Data Lake, and structured databases • Design data models and structures that enable efficient analytics and reporting • Design, develop, and maintain complex stored procedures, functions, triggers, and views • Implement database DevOps processes including schema versioning, deployment automation, and rollback procedures • Perform database performance tuning, indexing strategies, and query optimization • Ensure data integrity, security, and compliance with HIPAA/HITRUST requirements for PHI data • Manage database lifecycle across development, pre-production, and production environments utilizing RedGate Flyway • Design and implement ETL/ELT processes to integrate data from multiple sources • Build event-driven data processing solutions including message producers, consumers, and workers using Azure Service Bus, Event Grid, and related messaging services • Integrate with external REST APIs for data ingestion and develop internal data APIs using Python (FastAPI/Flask) on Azure Container Apps or Functions • Develop data transformation logic and orchestration workflows • Ensure data quality, consistency, and lineage across the data platform • Integrate with Azure AI services and machine learning endpoints as needed • Review designs of Power BI solutions including semantic models, reports, & dashboards • Collaborate with stakeholders to translate business requirements into technical solutions • Optimize data structures for reporting and analytics performance • Provide technical guidance and mentorship to junior developers and engineers • Contribute to data engineering best practices, standards, and patterns • Participate in architecture reviews and technical decision-making • Collaborate with DevOps and Infrastructure teams on platform improvements • Drive continuous improvement in data quality, performance, and reliability • Create and maintain comprehensive technical documentation including data dictionaries, architecture diagrams, and process flows • Document data lineage, transformation logic, and business rules • Ensure all data handling practices meet HIPAA/HITRUST compliance requirements • Maintain audit trails and documentation for compliance purposes • Troubleshoot and resolve complex data-related issues across the platform • Provide escalation support for production incidents • Collaborate with cross-functional teams to support business operations • Participate in on-call rotation as needed
• Design and build the AWS-native data foundation behind enterprise AI applications • Lead the design and evolution of the knowledge graphs and ontologies powering AI's reasoning, retrieval, and explainability • Align enterprise data into a coherent, queryable graph with clear provenance across structured, semi-structured, and unstructured sources • Curate grounding corpora, eval datasets, and retrieval benchmarks for LLM-based features • Build ingestion and transformation pipelines in Python and SQL using AWS services • Author infrastructure as code in CloudFormation and apply AWS best practices • Partner with security and platform teams to integrate data access with enterprise identity and access policies • Set the design direction for data and semantic modeling across the team
• Lead architecture assessment and documentation of the current data platform (Snowflake, Airflow, AWS) • Design the future-state data platform architecture following modern ELT and dbt best practices • Define standards for: data modeling, pipeline architecture, transformation frameworks, governance and data quality • Lead the migration of transformation logic into dbt models • Define architectural patterns for: staging layers, intermediate layers, data marts • Implement automated testing frameworks within dbt • Lead discovery sessions and technical workshops with the client’s data engineering teams • Document architecture, pipelines, and operational processes • Conduct architecture walkthrough sessions during knowledge transfer phases • Define standards for CI/CD, monitoring frameworks, and data quality validation • Establish schema management and governance best practices • Optimize Snowflake compute usage and platform performance • Design scalable orchestration strategies for Airflow pipelines • Improve platform reliability, monitoring, and operational observability
• Design and implement a scalable lakehouse medallion architecture within Databricks. • Establish ingestion, canonical, and business-ready data modeling layers with clear standards and naming conventions. • Build and maintain structured, governed fact and dimension models for enterprise reporting. • Oversee ingestion pipelines using Fivetran, Airbyte, and custom integrations while optimizing cost and performance. • Develop and manage SQL and Python transformation jobs including incremental processing, upsert logic, and snapshot modeling. • Implement orchestration, monitoring, data quality validation, and failure recovery processes. • Define enterprise data governance standards including access controls and reconciliation processes. • Integrate ERP, Shopify, GA4, Google Ads, Meta Ads, and catalog marketing data into unified customer and order models. • Lead and mentor a small team of data and analytics engineers while setting coding and review standards.
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