Data Engineer

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions. Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

Senior Data Engineer

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases
• Develop data transformations using Delta Lake and Lakehouse architecture best practices
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance
• Participate in migrating and modernizing legacy data processes to cloud-native architectures
• Improve data quality through validation, testing, monitoring, and automation
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification

Data Engineer II

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python.
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases.
• Develop data transformations using Delta Lake and Lakehouse architecture best practices.
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources.
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions.
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance.
• Participate in migrating and modernizing legacy data processes to cloud-native architectures.
• Improve data quality through validation, testing, monitoring, and automation.
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing.
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification.

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Drive the Data Architecture and Data platform modernization for the Workplace Investing division
• Work with Enterprise Architects to modernize legacy systems
• Collaborate closely with business partners and technology teams

Director, AI/ML, Data Architecture

• Develop and enhance canonical data models, semantic models, and domain‑driven data contracts to enable cross‑enterprise data interoperability.
• Produce conceptual, logical, and physical data models aligned to business capabilities, modernization goals, and data mesh principles.
• Design and implement data architecture patterns to support real‑time streaming solutions using Kafka.
• Partner with product owners, domain architects, and data engineering teams to translate business needs into scalable, well‑governed data models.
• Ensure consistent application of data governance, metadata management, and lineage standards across data domains.
• Architect and optimize data pipelines and storage solutions using AWS (S3, Redshift, Glue, EMR, Lambda) and Snowflake.
• Contribute architectural artifacts, modeling standards, and reusable templates for enterprise data modeling.
• Support enterprise initiatives around real‑time data integration, semantic mesh, and AI/ML enablement through strong data architecture foundations.

Data Architect

• Lead the design and evolution of Foley’s enterprise data architecture
• Contribute to the ongoing development of our Enterprise Data Model
• Define standards and best practices for data modeling, storage, access, and reliability
• Drive architectural decisions that balance scalability, maintainability, and performance
• Design, build, and maintain scalable data pipelines across internal systems, vendor platforms, and the data warehouse
• Improve data quality, observability, and reliability across core workflows
• Optimize data workflows for efficiency and long-term operational health
• Support both batch and event-driven or CDC-based pipeline patterns
• Own schema design and warehouse modeling strategy, including dimensional and normalized approaches where appropriate
• Help define and implement data governance best practices, including RBAC
• Ensure shared definitions, documentation, and trust in core business data
• Serve as a thought partner to Engineering, Product, Analytics, and business teams
• Translate technical complexity into clear recommendations and tradeoffs
• Raise the bar for data engineering practices across the organization
• Help the company make better decisions through strong, accessible data foundations
• Identify opportunities to improve workflows through automation, tooling, and thoughtful use of AI
• Experiment pragmatically with new technologies and scale what works
• Build systems and guardrails that enable teams to move faster without sacrificing quality

Staff Data Engineer

• Develop and maintain data pipelines, ETL processes, and workflows to collect, clean, and transform data from various sources into usable formats
• Ensure data integrity, quality, and accuracy throughout the data processing lifecycle
• Analyze and interpret health insurance exchange data and related datasets to identify patterns, trends, and insights
• Generate reports, dashboards, and visualizations to communicate findings effectively to non-technical stakeholders
• Design, develop, and maintain databases, data models, and schemas for efficient storage and retrieval of data
• Collaborate with cross-functional teams to understand business requirements and translate them into data solutions
• Stay up to date with emerging technologies, industry trends, and best practices in data analysis, engineering, and visualization
• Identify opportunities to improve data infrastructure, processes, and analytics capabilities

Data Engineer jobs in Rhode Island

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Senior Data Engineer

Senior Data Engineer

Data Engineer II

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Data Engineer II

Data Engineer

Director, AI/ML, Data Architecture

Data Architect

Staff Data Engineer

Data Engineer