Data Engineer

• 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

• Accountable for leading the execution of the Data Platform portfolio of work.
• Set direction on the technical architecture, patterns, and practices used by the team.
• Prioritize work based on its long-term ability to provide value with incremental releases.
• Develop a multi-quarter product roadmap that articulates a clear vision and strategy for the data platforms.
• Manage the data platforms throughout their lifecycle.
• Ensure a high-quality architecture for all data from all sources using consistent patterns and practices.
• Assist the Engineers on the team with setting priorities, making decisions, and removing roadblocks.
• Setup the support process for each platform and product aligned to the overall support strategy.
• Ensure platform performance, stability, and cost are proactively managed.

Data Platform Manager

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.
• Collaborates with analytics and business teams to create and improve data models that feed BI tools, increasing data accessibility, and fostering data-driven decision making across the organization.
• Takes ownership of the health, reliability, and security of the data platform by designing and implementing processes and solutions that proactively monitor data delivery across the full pipeline — ensuring data is accurate, complete, consistent, and timely from source ingestion through gold layer curation.
• Investigates, diagnoses, and resolves complex data pipeline failures across all system layers; uses findings to drive pipeline improvements and communicates status, root cause, and resolution clearly to the team throughout the full lifecycle of an incident.
• Assists with the development and delivery of data solutions, including data pipelines, API integration, and other structures and processes to support the data platform
• Identifies, designs, and implements internal process improvements, including the automation of manual processes, optimization of data delivery, and refactoring of code for improved performance
• Builds pipelines to enable the optimal extraction, transformation, and loading of data from a wide variety of data sources
• Provides strategic assistance to BI leadership and the BI team by making recommendations in the areas of data management and best practices
• Collaborates with developers, database administrators, data architects, and others on reporting and data management projects as needed
• Takes on other duties needed to help drive our Purpose, fulfill our Brand Principles, and abide by our Organization’s Values

Data Engineer II

• Collaborate with other Engineers and Architects to define the enterprise data architecture roadmap.
• Drive the design and optimization of OLTP and OLAP models for scalability, performance, and maintainability.
• Establish standards for data modeling, storage, and integration across systems.
• Partner with other key stakeholders within engineering in developing a comprehensive data and BI strategy aligned with business objectives.
• Define data governance practices, data access control, compliance, and quality standards.
• Lead proof-of-concepts (POCs) for new data technologies and frameworks.
• Own the architectural review process and guidance to development teams on data-related projects.
• Establish and ensure best patterns and practices for ORM usage (Entity Framework and others) and SQL optimization, according to business needs.
• Evaluate and recommend where AI/ML can enhance data-driven decision-making.
• Stay current with modern data engineering trends, cloud-native architectures, and big data platforms.
• Actively participate and take ownership of Data Architecture artifacts via ADR process.
• Drive AI adoption by introducing and guiding teams in leveraging AI capabilities within the data engineering space to enhance internal efficiency, innovation, and decision-making in everyday workflows.
• Liaison between technology and product teams assisting teams in translating business requirements into tangible architectures that meet business needs now in and in the future.
• Design data architectures that accurately reflect and support evolving complex product and business processes and are required by our customer base.
• Lead data architecture initiatives, collaborating with cross functional teams, from concept through implementation, including governance and quality frameworks.
• Identify opportunities for predictive analytics and advanced modeling.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Data Architect

• Lead Financial data modeling and data product delivery
• Oversee the design and buildout of ETL/ELT data pipelines
• Implement best practices for performance tuning and data governance
• Create documentation for data pipelines, workflows, and processes
• Share best practices and mentor junior data engineers

Senior Manager, Data Engineering

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Lead and support the organizational data integration efforts by effectively developing and leading a team of data integration developers, engineers, architects, and managers.
• Establish enterprise data architecture standards, canonical data models, data domains, and data product strategies.
• Lead the modernization of legacy SQL Server ETL workloads into Azure Databricks and Lakehouse architectures.
• Define and govern Bronze, Silver, and Gold data layer standards.
• Establish enterprise data dictionaries, business glossaries, metadata management, and lineage standards.
• Lead development of AI-ready data products supporting machine learning, predictive analytics, intelligent automation, RAG, and agentic AI solutions.
• Define enterprise DataOps practices including CI/CD, automated testing, observability, data quality, and deployment automation.
• Lead the design and implementation of data integration and data lake house solutions.
• Lead collaboration efforts with IT teams to ensure robust and scalable data architecture is established and meeting company objectives.
• Lead the establishment of data validation and reconciliation processes to maintain data accuracy.
• Partner with business stakeholders to understand data requirements and deliver solutions that meet their needs.
• Assess the current data services processes, identify challenges, quantify the business value, establish procedures to address challenges, and support the future state model and vision definition.
• Stay current with industry trends and advancements in data integration and management technologies.
• Lead healthcare data integration initiatives involving claims, eligibility, pharmacy, clinical, financial, operational, and partner data sources.
• Serve as technical authority for data modeling, canonicalization, master data management, and enterprise data governance practices.
• Provide direct leadership, coaching, hiring, and performance management for Data Engineers and Data Integration Engineers.
• Participate in architecture reviews and remain actively engaged in solution design, platform modernization, and technical delivery.
• Develop training plans to foster growth and development across functional areas to meet expanding technology needs.
• Research and develop learning needs for ongoing system developments with contractors.
• Continuously review and monitor technology resources and gap analysis, establish criteria and make recommendations for advancement.
• Develop and implement a comprehensive data management strategy aligned with strategic objectives.
• Establish resources, tools and direction for each functional leader.
• Establish data integration policies and procedures to ensure data accuracy, security, and compliance with regulatory requirements.
• Participate in, adhere to, and support compliance, people and culture, and learning programs.
• Perform other duties as assigned.

Manager, Data Engineering

• Design and implement lake house architectures utilizing Azure Databricks, Delta Lake, Azure Data Lake Storage Gen2, Unity Catalog, and related modern data platform technologies
• Develop canonical enterprise data models, business entity mappings, master data structures, and reusable data products supporting enterprise reporting, analytics, automation, and AI initiatives
• Build and maintain bronze, silver, and gold data layers supporting governed, trusted, and AI-ready datasets
• Develop and maintain metadata, data dictionaries, business glossaries, lineage documentation, and data catalog integrations supporting enterprise data governance
• Design, develop, and maintain ETL pipelines for structured and unstructured data across cloud and on-prem environments

• Oversight and responsibility for designing, building, and maintaining scalable data pipelines and architectures that enable reliable data access, analytics, and reporting
• Partners closely with analytics, data science, product, and business teams to ensure high-quality, well-structured data is available for decision-making and operational needs
• Design, build, and maintain scalable data pipelines and ETL/ELT processes
• Develop and optimize data models, data warehouses, and data lakes by working with stakeholders to understand their requirements and translate them into scalable and efficient data engineering solutions
• Build, optimize, and deploy forecasting models using time-series modeling, machine learning, and MLOps practices
• Apply advanced modeling and statistical analysis to Healthcare Quality metrics spanning NCQA HEDIS measures and CMS Clinical Stars
• Performs code reviews and enforces coding standards, based on company and industry requirements, across development teams
• Ensure data reliability, accuracy, performance, and availability across systems
• Integrate data from multiple sources including internal systems, APIs, and third-party vendors
• Implement data quality checks, monitoring, and error-handling processes
• Collaborate with analytics, BI, and data science teams to support reporting and advanced analytics
• Optimize query performance and storage efficiency
• Document data pipelines, schemas, and engineering standards
• Support data governance, security, and compliance requirements
• Troubleshoot and resolve data pipeline and system issues

• Design and implement robust ETL/ELT pipelines
• Build and maintain Feature Stores and specialized datasets
• Develop the data pipelines required for Generative AI
• Act as the technical lead for our Snowflake data warehouse
• Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory
• Partner directly with central IT, Database Administrators, and Security teams
• Implement automated validation and observability layers
• Drive the efficiency of our data stack by optimizing storage
• Work as a dedicated engineering partner to MLOps and Data Science teams

Data Engineer, AI

• Perform Data Warehouse Architecture at an intermediate level
• Design and develop data models and ETL specifications
• Conduct data analysis and recommend solutions
• Participate in data warehouse and dashboard architecture planning
• Propose requirements, standards, and best practices for data modeling and ETL
• Conduct product and vendor research and present recommendations

IS Data Warehouse Architect II

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Oversight and responsibility for designing, building, and maintaining scalable data pipelines and architectures
• Partner closely with analytics, data science, product, and business teams to ensure high-quality, well-structured data is available for decision-making and operational needs
• Design, build, and maintain scalable data pipelines and ETL/ELT processes
• Develop and optimize data models, data warehouses, and data lakes by working with stakeholders
• Build, optimize, and deploy forecasting models using time-series modeling, machine learning, and MLOps practices
• Apply advanced modeling and statistical analysis to Healthcare Quality metrics spanning NCQA HEDIS measures and CMS Clinical Stars
• Integrate data from multiple sources including internal systems, APIs, and third-party vendors
• Collaborate with analytics, BI, and data science teams to support reporting and advanced analytics

• Define and implement enterprise-wide AI data architecture strategies to enable secure, scalable, and compliant AI/ML adoption across the organization.
• Design canonical data models, metadata frameworks, and pipelines to support AI/ML model development, training, deployment, and monitoring.
• Establish standards for data quality, lineage, master data management (MDM), and “golden record” frameworks to ensure reliable AI outputs.
• Collaborate with data governance, compliance, and security teams to enforce policies for ethical and responsible AI data usage.
• Architect and oversee AI-ready data platforms (cloud, hybrid, and on-prem) to integrate structured, unstructured, and streaming data sources.
• Implement modern data architectures (e.g., data mesh, data fabric, Lakehouse, Apache Iceberg/Delta Lake) to accelerate AI/ML projects.
• Optimize AI/ML workloads on cloud platforms (AWS, Azure, GCP) and manage data pipelines using tools like Kafka, Glue, Spark, and Snowflake.
• Partner with AI engineers, data scientists, and business leaders to align data architecture with financial products, regulatory compliance, and risk management needs.
• Provide architectural leadership in modernization efforts (real-time payments, fraud detection, personalization engines, and regulatory reporting).

Data Engineer jobs in Idaho

See all jobs on JobTailor

Senior Software Engineer, ERP, Data & Domain

Data Platform Manager

Staff Data Engineer

Data Engineer II

Data Architect

Senior Manager, Data Engineering

Director of Engineering – Data Platform

Manager, Data Engineering

Data Engineer

Manager, Data Engineering

Data Engineer, AI

IS Data Warehouse Architect II

Supply Chain Data Engineer

Manager, Data Engineering

AI Data Architect