Data Engineer

• Support scalable data operations through development of ETL processes, SQL-based integrations, Power Platform solutions, and Power BI reporting capabilities.
• Design, build, maintain, monitor, and troubleshoot data-processing automations.
• Develop and maintain data ingestion pipelines from external sources into SQL databases.
• Manage automated flows to trigger Logic Apps and handle lightweight processes.
• Perform full-stack BI development including data modeling, DAX development, and report publishing.
• Leverage Microsoft Fabric as the unified access platform.
• Ensure alignment with security and compliance requirements.
• Conduct root-cause analysis to reconcile discrepancies between systems.

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data—then implement it in production.
• Build mission-critical pipelines. Develop and operate batch data workflows that process high-volume events related to notifications with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and production applications.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, self-healing, maintainable data assets.

Data Engineer, Notifications

• Architecting high-scale, secure and interoperable data platforms across the enterprise to enable AI and analytics applications
• Architecting data domains and designing data products to be shared across the enterprise
• Architecting cross-segment semantic layer and data catalog solutions
• Designing an agentic architecture for exposing data from enterprise data lakehouse domains to AI applications
• Developing MDM strategy and architecting MDM solutions for complex enterprise domains (e.g., Finance, HR)

Lead Data Architect

• Define and evolve enterprise data architecture principles, standards, and reference architectures aligned to business priorities and modern practices
• Lead the design and maintenance of enterprise conceptual, logical, and physical data structures, ensuring consistent definitions and shared semantics
• Establish domain-oriented architecture patterns for reusable, governed data products and interoperable data flows
• Develop architecture patterns supporting both batch and streaming workloads, including event-driven approaches and canonical representations
• Partner with data engineering teams to translate architecture and logical designs into scalable physical implementations on AWS cloud data platforms
• Ensure consistent data definitions, lineage, and metadata documentation across domains by enabling cataloging and lineage platforms
• Maintain a central repository of architecture artifacts, definitions, and metadata documentation
• Influence data design decisions across programs and projects through architecture reviews and standards enforcement
• Facilitate architecture and data design workshops with business and technical stakeholders
• Lead, mentor, and develop a team of data architects and specialists, ensuring consistent delivery of architecture outcomes
• Stay current on evolving architecture trends and evaluate their applicability to Deckers

Director, Data Architecture

• Lead the design and evolution of the data platform architecture, establishing patterns and standards the team builds on.
• Build and operate production-grade data pipelines that ingest and transform high-variance, real-world clinical data reliably and at scale.
• Design for automation from the start: pipelines that detect problems, recover gracefully, and surface issues without requiring manual intervention to run.
• Contribute to quarterly data product releases, working closely with product, clinical, customer success teams to meet commitments.
• Build data quality tests that reflect the evolving needs of our downstream consumers.
• Mentor and elevate other data engineers through code review, architecture decisions, and shared standards.
• Actively use and advocate for AI tools that improve the team's development velocity and code quality.

Senior Data Engineer

• Build and maintain high‑performance streaming and batch data pipelines that power AI applications
• Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants
• Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance
• Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements
• Integrate pipelines with shared AI platform services (agent frameworks, registries, feature stores), ensuring clean, versioned, and reliable data delivery
• Build reusable ingestion, transformation, and data processing components that streamline adoption across engineering teams
• Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis
• Ensure deployed pipelines meet high standards for reliability, recoverability, auditability, and long‑term maintenance
• Drive execution by owning the full development lifecycle: prototyping, implementation, testing, deployment, optimization, and documentation
• Collaborate closely with infrastructure, ML engineering, product, and governance teams to deliver production‑ready AI capabilities
• Lead by example through strong execution, high‑quality code, and proactive problem solving

• Lead workspace architecture, Unity Catalog governance, and cluster policy design for client tenant organizations.
• Perform tenant discovery and requirements gathering, source profiling, and security classification for new data intake requests.
• Develop end-to-end technical designs for tenant onboarding, including Data Sharing Agreements and SLA documentation.
• Maintain Requirements Traceability Matrix (RTM) and prepare Architecture Review Board (ARB) submissions.
• Contribute to tenant onboarding automation, financial operations integration, and ROI reporting.
• Deliver tenant training materials and facilitate weekly office hours for client program teams.
• Support P1/P2 incident response within SLA for tenant-related platform issues.
• Provide NIST 800-53 control evidence within 5 business days of the client audit requests.

• 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

• Own the roadmap for the CSS data platform
• Establish architectural principles and governance standards
• Drive convergence across product analytics, ML infrastructure, and AI data foundations
• Represent data engineering at the executive level
• Set and uphold the technical bar for distributed data systems
• Lead and grow multiple engineering teams
• Build a culture of ownership and accountability
• Build deep partnerships across various teams
• Influence Salesforce-wide standards for data and AI engineering

Director, Data Engineering – Customer Success Score

• Developing integrations between our ERP and other enterprise systems
• Building Power BI reports and semantic models that drive business decisions
• Contributing to our Microsoft Fabric data warehouse
• Ensuring data flows reliably across systems and is accessible, accurate, and actionable
• Designing and building integrations between the new ERP and enterprise systems
• Migrating and redesigning Power BI reports to support the new environment
• Leveraging AI tools to accelerate development, documentation, and data analysis

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Designing and developing modern, data-centric applications that support clinical and operational workflows
• Building scalable data pipelines, transformation and enrichment processes, and intuitive user interfaces
• Mentoring junior Data Engineers

Data Engineer I

• Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Monitor work against production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Collaborate on the design and implementation of data security measures, including access controls, encryption, and data masking
• Mentor other associate and intermediate data engineers as needed
• Perform data analysis and provide insights to support decision-making across various departments
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed
• Other duties as assigned or requested.

• responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks.
• owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
• partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.
• accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.
• primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

Lead Data Engineer – Modernization, Reliability

• 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

• Establish and evolve the enterprise data architecture vision aligned to Autodesk’s business strategy
• Define enterprise data domains and ownership models across operational, customer, and analytical ecosystems
• Develop guiding principles and guardrails for how data is created, shared, mastered, governed, and activated
• Shape architectural patterns that support data products, domain-oriented ownership, and modern data paradigms
• Identify enterprise data assets that can be activated to power AI/ML, personalization, automation, and intelligent decisioning
• Design architectural patterns that make high-quality, governed data accessible for AI workflows across the organization
• Ensure machine learning pipelines and AI systems are grounded in authoritative enterprise data domains
• Partner with AI/ML and product teams to define scalable, reusable data foundations for experimentation and production AI systems
• Remove systemic barriers that limit data discoverability, accessibility, and reuse
• Create and maintain high-level conceptual and logical data models spanning CRM, ERP, Finance, subscription systems, marketing platforms, support systems, and analytics environments

Senior Data Architect – Enterprise Data Strategy

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Own the design of data models and structures for analytics purposes; collaborate with our consulting partner and internal team members to bring them to life and iterate based on what actually works in practice.
• Design data ingestion and transformation pipelines that are maintainable, observable, and appropriately documented—not just functional at launch.
• Define the security model for data access, with genuine attention to the intersection of data privacy requirements, compliance obligations, and the usability needs of internal teams.
• Lead tool evaluation and selection across ingestion, data lake, data governance, and data catalog—with a process that produces clear, reasoned recommendations, not just vendor comparisons.
• Ingest priority data sources into our data warehouse or lake and prepare them for analytical use, including validating that the data is trustworthy before downstream teams depend on it.

Data Architect

• Collaborate with Product Management, Servicing, Sales, Fraud, Marketing, and UX teams to understand use cases and segmentation needs.
• Work closely with internal stakeholders to understand their data needs and translate those needs into user stories for Data & Analytics, technical, and engineering teams.
• Help develop a comprehensive CDP implementation strategy aligned with overall product and customer success goals.
• Identify new data sources and onboard existing data sources to the CDP.
• Evolve our identity resolution models (deterministic and probabilistic).
• Build and run queries to answer questions and build segment and build solutions for users of CDP to be able to self-serve.
• Be the CDP subject matter expert, gathering information to create accurate, informative, and resonant user stories for both the business and the data team.
• Deeply understand customer needs to define priorities, inspire the broader team, and deliver best-in-class experiences and enabling capabilities.
• Help build the product roadmap, define user stories, and drive trade-off decisions.
• Identify new products and features on behalf of the customer, leading 1-2 scrum teams to align on mission and deliver great results iteratively.
• Leverage qualitative and quantitative data to measure results, inform the roadmap, and achieve benefit, adoption, and financial results.
• Collaborate with Product, Analytics, Engineering, and Business Leaders to drive scalable data solutions.
• Present effectively to high-level stakeholders, set clear priorities and direction, and build bridges across groups.
• Own the product end-to-end by developing business models, defining and analyzing success metrics, and managing strategic projects.
• Champion and educate product managers and lines of businesses on what the GEICO CDP can do for their business.
• Lead the CDP Squad in achieving strategic outcomes with solutions that are Valuable, Viable, Usable and Feasible.

Principal Product Manager – Customer Data Platform

• Design and maintain enterprise data architecture strategies and scalable data solutions
• Lead architecture discussions with technical teams and business stakeholders
• Evaluate existing systems and recommend modernization opportunities
• Design logical and physical data models to support operational and analytical use cases
• Lead data integration, transformation, and interoperability initiatives
• Define standards and best practices for data management, governance, and architecture
• Support enterprise data lake, warehouse, and reporting initiatives
• Develop and maintain architecture documentation, diagrams, and technical standards
• Ensure solutions align with security, scalability, and performance requirements
• Collaborate with engineering teams on implementation and delivery strategies
• Support data lifecycle management and long-term maintainability
• Mentor technical teams and provide architecture guidance

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Data Engineer

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Data Engineer I

Senior Data Engineer

Lead Data Engineer – Modernization, Reliability

Data Engineer

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Data Engineer

Data Architect

Principal Product Manager – Customer Data Platform

Senior Data Architect