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

Senior Data Engineer

• 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

• Develop and execute Summit Credit Union's enterprise data strategy to support business and technology objectives.
• Design, build, and maintain data architectures, databases, data models, and integration solutions that enable scalable and reliable data management.
• Partner with business leaders, developers, engineers, and IT teams to understand data needs and deliver solutions that drive business value.
• Establish and maintain data governance practices that ensure data quality, consistency, security, and regulatory compliance.
• Analyze complex datasets to identify trends, answer business questions, and support informed decision-making.
• Leverage historical data and analytics to develop insights and predictive models that support future business outcomes.
• Recommend and implement data tools, technologies, and best practices to improve data capabilities and operational efficiency.
• Develop and monitor key performance indicators (KPIs) and metrics related to data consumption, integration, and utilization.
• Ensure data is protected against unauthorized access while remaining accessible to authorized users.
• Exercise independent judgment when evaluating technologies, data solutions, and business opportunities.
• Communicate technical concepts and insights effectively to both technical and non-technical audiences, including senior leadership.

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets.
• Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
• Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.

Staff Software Engineer – DC Tech Lead

• Design, build, and maintain robust data pipelines and data lake architecture for both batch and real-time streaming use cases, including high-volume, low-latency data processing
• Improve observability, alerting, and SLOs across data systems so pipelines are easier to monitor and issues are caught early
• Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
• Develop dbt workflows to onboard Evaluation Partners and create end-of-day reporting for partner analysis
• Build and support event-driven architectures and scalable platform components
• Contribute to the orchestration and automation of workflows
• Integrate complex financial APIs and third-party data sources into internal systems
• Collaborate with analytics, product, and ML engineers to develop and deploy reliable data products
• Work on feature pipelines and model-ready data to support ML engineers
• Promote high standards in code quality, testing, and platform reliability
• Participate in Agile ceremonies and foster a collaborative, growth-oriented team culture

• Solving our unique data-lake challenges: transforming and seamlessly normalizing highly varied partner datasets (such as donation data);
• Design robust batch-processing pipelines capable of extracting and loading massive datasets from a variety of internal, external, and public sources;
• Developing processes for data mining, data modeling, and data production;
• Leveraging modern technology knowledge to champion evolving industry trends, updated design patterns, and engineering excellence across the team;
• Autonomously scope, design, and execute complex data projects from day one, turning ambiguous requirements into clear, decisive technical plans;
• Developing robust testing and monitoring systems for scheduled processes;
• Collaborating with cross-functional teams to support their data infrastructure needs;
• Joining each and every one of your colleagues in creating an inclusive workspace.

Staff Data Engineer

• 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 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 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

• 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

• Develop and optimize data and data pipeline architecture
• Provide thought leadership to support software developers and data scientists
• Assemble large, complex data sets that meet functional requirements
• Identify and implement internal process improvements
• Build AWS infrastructure for data extraction and transformation
• Automate deployment and management of data services using IaC
• Contribute to CI/CD processes
• Develop analytics tools for actionable insights

Senior Data Engineer – Temp to Hire

• Assist in the design, build, testing, deployment, and production support of core data warehouse and analytics solutions
• Perform initial data exploration steps such as profiling, understanding data quality, binning, pivoting, and summarizing
• Consult with data SMEs to understand and address data quality issues
• Support the design, build, test, and deployment of data warehouse solutions
• Provide modeling analysis and develop data models according to specifications and standards
• Support the data cleaning, integration, and transformation of data according to the defined target data model

Data Warehouse Developer

• Collaborate with scientific teams to design scalable data capture systems and user-friendly visualizations of experimental results
• Design and implement database architectures, data models, and automated data pipelines across platforms (e.g., Amazon Web Services, Laboratory Information Management Systems, Benchling, and contract research organization systems)
• Develop structured data models capturing microbial strain metadata, growth conditions, and experimental outputs
• Partner with researchers to standardize experimental metadata, ontologies, and data structures to improve data quality, reproducibility, and traceability
• Build and maintain data warehouses supporting probiotic growth, strain characterization, and clinical datasets
• Develop and implement robust data quality and integrity checks across data ingestion workflows, including integration of externally generated clinical data
• Build pipelines integrating multi-modal datasets (e.g., sequencing, metabolomics, in vitro assays, and clinical endpoints)
• Partner cross-functionally to prototype and deploy digital solutions that enhance research and development efficiency and scalability

Life Science Data Engineer

• 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

• Define and maintain the technical vision, architecture principles, and engineering strategy for the Solution Data Repository.
• Lead the design and development of an enterprise ontology to inform the technical architecture.
• Drive practical interoperability between catalog/SDF sources and downstream tools.
• Implement and enforce engineering lifecycle standards, code quality practices, release discipline, and operational readiness criteria.

Enterprise Data Architect – Solutioning Suite

• 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

• Develop scalable, well‑documented ETL/ELT pipelines using T‑SQL, Python, Azure Data Factory/Fabric Data Pipelines, and Databricks; implement best‑practice patterns for performance, security, and cost control.
• Design relational and lakehouse models; create Fabric OneLake shortcuts, medallion‑style layers, and dimensional/semantic models for Power BI.
• Build automated data‑quality checks, lineage, and observability metrics; contribute to CI/CD workflows in Azure DevOps or GitHub.
• Gather requirements, demo iterative deliverables, document technical designs, and translate complex concepts to non‑technical audiences.
• Research new capabilities, share findings in internal communities of practice, and contribute to reusable accelerators.
• Collaborate with clients and internal stakeholders to design and implement scalable data engineering solutions.

• Define and drive the product vision, strategy, and roadmap for the Data Curation Pipeline team
• Lead the modernization of healthcare data pipelines that ingest and transform raw claims, prescription, and EHR data into standardized enterprise datasets
• Identify opportunities to improve data quality, scalability, usability, speed, and downstream product readiness
• Partner with internal stakeholders to understand business needs and translate them into product priorities and roadmap decisions
• Own and manage the team backlog, ensuring clear prioritization of high-impact initiatives, technical enablers, defects, and enhancements
• Serve as the primary product point of contact for engineering, architecture, and cross-functional stakeholders
• Define acceptance criteria and participate in user acceptance testing and release validation
• Work closely with software developers, data engineers, architects, QA, analytics teams, and business stakeholders throughout the product lifecycle
• Lead Agile product processes including backlog refinement, sprint planning input, prioritization, and stakeholder communication
• Build strong relationships with internal business stakeholders, product teams, and data consumers across the organization

Data Engineer jobs in Wisconsin

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Senior Data Engineer

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Data Engineer

Staff Software Engineer – DC Tech Lead

Senior Data Engineer

Staff Data Engineer

Data Architect

Director of Engineering – Data Platform

Staff Data Engineer

Senior Data Engineer, Data Platform Admin

Senior Data Engineer – Temp to Hire

Data Warehouse Developer

Life Science Data Engineer

IS Data Warehouse Architect II

Supply Chain Data Engineer

Enterprise Data Architect – Solutioning Suite

Data Engineer

Data Engineer

Senior Product Manager, Data Platform – IntelliScript