Data Engineer

• Design and implement enterprise semantic and ontology models that enable advanced analytics, AI, and LLM-driven solutions.
• Establish and maintain enterprise data quality and validation frameworks that identify, monitor, and communicate data integrity issues, ensuring trusted and reliable data across the organization.
• Partner with business and technology stakeholders to design data pipelines and solutions that align with evolving business needs and strategic objectives.
• Architect scalable data engineering patterns and optimize data solutions for performance, scalability, observability, and long-term maintainability across data warehouses and lake houses.
• Develop and maintain CI/CD processes, deployment standards, and engineering best practices that support reliable and efficient delivery of semantic models and data solutions.
• Integrate data lineage, metadata management, and observability capabilities into data solutions to improve transparency, traceability, and operational effectiveness.
• Contribute to enterprise data governance, security, and compliance initiatives by implementing data access controls, including Row Level Security (RLS), and promoting adherence to organizational standards.
• Provide technical leadership through code reviews, mentoring, and knowledge sharing to elevate engineering practices and support the development of data modeling and analytics engineering capabilities.

Senior Data Engineer

• The Data Engineer will support the design, development, and implementation of analytical data processes that enable actuarial pricing, reserving, and claims analytics across a Property & Casualty insurance business.
• This role focuses on building and maintaining scalable data pipelines, models, and tools under established architectural patterns and standards.
• The Data Engineer will partner with actuarial, data science, and finance teams to deliver reliable and well-structured data solutions that support analytical workflows and business insights.
• Develop and maintain analytical pipelines and tools that support actuarial analysis.
• Build and enhance data models that provide consistent, structured access to actuarial data.
• Support the development of applications and tools used in pricing, reserving, and claims analytics.
• Support the adoption of best practices for end-to-end analytical data process flow.
• Contribute to the continuous improvement of data workflows by identifying inefficiencies and recommending enhancements.
• Apply data validation, reconciliation checks, and quality controls within data pipelines.
• Assist in maintaining data lineage and documentation to support transparency.
• Support experimentation with emerging AI capabilities in process flow design.

Actuarial 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

• 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

• Serve as a hands-on technical leader responsible for designing, building, and optimizing Cambridge's modern data platform.
• Help define data architecture standards, lead engineering efforts, mentor team members, and drive scalable data solutions.
• Design, develop, and maintain scalable data pipelines supporting enterprise reporting, analytics, and business applications.
• Build and optimize ETL/ELT solutions using modern cloud-based technologies.
• Design and implement data integration solutions utilizing Azure Data Factory (ADF), Azure Functions, and other Azure services.
• Develop and maintain datasets within Snowflake and Azure Data Lake Storage (ADLS) environments.
• Lead data platform architecture initiatives, including data modeling, ingestion, transformation, storage, and consumption patterns.
• Drive performance tuning and cost optimization efforts across cloud data platforms and processing workloads.
• Design secure, reliable data movement solutions using batch and streaming integration patterns.
• Establish engineering best practices, code reviews, and architectural standards.
• Mentor data engineers and provide technical leadership across projects.
• Partner with analytics, product, and business stakeholders to deliver scalable, high-quality data solutions.

Principal Data Engineer

• Define and own the data architecture strategy across all environments, platforms, and data domains.
• Establish and maintain architectural standards, patterns, and best practices for data ingestion, storage, modeling, and consumption.
• Design scalable, secure, and resilient data architectures on AWS, including data lake, lakehouse, warehouse, and serving layers.
• Define and champion a Medallion (bronze/silver/gold) architecture, establishing standards for raw, refined, and curated data layers and the promotion patterns between them.
• Lead the platform direction of AgencyBloc's cloud data warehouse/lakehouse, defining how each is used, where workloads run, and how cost and performance are managed.
• Establish data modeling standards (dimensional, Data Vault, and other patterns) that balance flexibility, performance, and maintainability.
• Define standards for batch and streaming pipelines, ETL/ELT frameworks, orchestration, and reuse patterns across teams.
• Lead tool and platform selection decisions (warehouse/lakehouse, ingestion, transformation, orchestration, catalog, BI), ensuring alignment with long-term strategy.
• Conduct architecture reviews and provide guidance on complex or high-impact data initiatives.
• Collaborate with engineering leadership to align data capabilities with product and business priorities.
• Define and own the data governance strategy, including data cataloging, lineage, classification, and ownership models.
• Establish data quality standards and frameworks, including validation, monitoring, and remediation practices.
• Partner with security and compliance teams to enforce secure-by-design principles for sensitive and regulated data, including access controls, encryption, masking, and PII handling (SOC 2 and relevant standards).
• Define data retention, archival, and lifecycle management standards.
• Partner with the DevOps Architect to define data pipeline observability standards, including monitoring, alerting, and SLAs/SLOs for freshness, completeness, and reliability, ensuring alerts surface high-quality, actionable signals tied to business impact with minimal noise.
• Establish practices for cost monitoring and optimization across data platforms.
• Drive standardization and reduction of tool and pattern fragmentation across data teams.
• Mentor data engineers and senior data engineers, providing technical leadership and architectural guidance.
• Define the organization's approach to enabling AI and ML workloads on the data platform, including feature data, governance, and adoption strategy.
• Establish patterns for delivering trusted, well-governed data to AI/ML and analytics use cases.
• Evaluate emerging data and AI capabilities and incorporate them into the platform roadmap where they provide measurable value.

Data Architect

• 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

• Develops, tests, and maintains complex prioritized engineering (design/build/test) work as a member of a data analytics product pipeline architecture and infrastructure team
• Investigates and analyzes feasibility of data integration prototypes and program requirements
• Develops specifications and plans the full range of programming actions to produce data integration components
• Develops and maintains complex automated ETL/ELT pipeline architecture that cleans, transforms, and aggregates data from disparate sources using architecturally approved tools, programming languages, and services to deliver a data mesh approach
• Develops and maintains complex automated ETL/ELT monitoring and alarming solutions using architecturally approved languages and services
• Monitors, troubleshoots, and optimizes data pipelines for quality, performance, and reliability creating a single source of truth providing business customers with highly reliable and quality data
• Builds complex unit tests to enable test driven development and automation to drive code reliability
• Develops, maintains, tests, and deploys code with architecturally approved toolsets using Dev Ops practices, ETL/ELT pipelines, data catalogs and data queries
• Maintains data solutions in a divest state to ensure business functionality until replaced with new technology
• Balances and designs data analytics product solutions based on cost and performance to meet data needs and business requirements
• Guides business customers to develop, troubleshoot, and optimize complex SQL and ETL/ELT solutions to solve reporting, metrics, and analytics problems
• Collaborates with cross-functional teams to understand data requirements for data analytics products and translate them into scalable and efficient data solutions

Data Engineer III

• Own day-to-day administration, configuration, and health of Oura's global Databricks environment, serving stakeholders across IT, business, and data science domains.
• Manage workspace governance: access controls, cluster policies, cost monitoring, job scheduling, and security configurations.
• Support onboarding of new teams and use cases onto the Databricks platform as Oura unifies data across Finance, SDM, Commerce, Marketing, and other domains.
• Proactively identify and resolve performance bottlenecks, reliability issues, and cost inefficiencies.
• Maintain SLAs for platform availability and incident response across global time zones.
• Partner with IT and Data Architecture teams to align platform configurations with Databricks best practices and Oura's Data Mesh approach.

Senior Data Engineer, Data Platform Admin

• Implement and optimize ETL process to ensure data accuracy, consistency, and scalability.
• Support the buildout, migration, and maintenance of the company’s Enterprise Data Lake house.
• Work with senior engineers to design and develop data pipelines that extract data from various sources, transform it into the desired format, and load it into the appropriate data storage systems.
• Work closely with Information Technology teams to implement continuous integration and deployment (Cl/CD) pipelines using Azure DevOps.
• Assist in administering and optimizing Azure Databricks clusters and ensure efficient data processing.
• Collaborate with analysts and business units to design and implement efficient data models.
• Determine data tracking and storage needs, perform data auditing and validation, perform data mapping, loading and conversion planning, and resolve data issues or discrepancies.

• 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

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

Data Engineer jobs in Iowa

See all jobs on JobTailor

Senior Data Engineer

Actuarial Data Engineer

Senior Software Engineer, ERP, Data & Domain

Staff Data Engineer

Data Engineer II

Principal Data Engineer

Data Architect

Director of Engineering – Data Platform

Data Engineer III

Senior Data Engineer, Data Platform Admin

Data Engineer

Supply Chain Data Engineer

Data Engineer