Data Engineer

• Lead the implementation of AI data pipelines integrating structured, semi-structured, and unstructured data to support AI and agentic solutions
• Develop AI-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
• Implement and optimize Retrieval-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
• Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
• Develop and maintain real-time data streaming pipelines using technologies such as Snowpipe
• Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
• Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
• Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
• Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
• Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
• Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise-wide practices
• Develop and integrate graph database solutions to support complex data relationships within AI systems
• Apply GenAI approaches to insurance-specific data use cases and challenges
• Lead the development of AI-ready data foundations that support scalable, production-grade solutions
• Ensure data platforms remain resilient, governed, and cost-efficient, aligned with enterprise cloud and data strategies
• Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
• Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution.

Senior Staff AI Data Engineer

• Define the technical architecture and platform standards for our lakehouse on AWS: distributed cloud architecture, schema conventions, multi-tenant isolation, and integration design
• Lead design and delivery of the production pipelines that consolidate performance and product data, and own data modeling for complex entities (time-series, hierarchical, multi-source) so the models serve products, analytics, and ML
• Introduce just enough data governance, ownership, and stewardship to raise our data maturity, and lay the catalog and semantic-layer foundation that analytics, ML, and AI agents can reason over
• Author and maintain the Data Platform playbook (reusable patterns, ADRs, runbooks, Terraform modules) with data quality and reliability built in, so product teams can self-serve new datasets and integrations
• Lead delivery end to end, from requirements and planning through coordinating workstreams and translating status to senior leadership and non-technical partners
• Mentor engineers across levels, raise the bar through design review and on-call ownership, and be the engineering voice shaping the platform roadmap

Staff Data Engineer

• Lead the design and delivery of database engineering solutions
• Perform applications database administration services including SQL code review, performance analysis, and database application design
• Establish database standards for operations, upgrades, migrations and onboarding new applications
• Interface with enterprise teams for Systems Administrators, Cyber Security and Capacity Planning groups
• Write clear, concise, well organized technical documentation to maintain standards and procedures

Principal Data Engineer

• Lead a team of data engineers to build and operate scalable, reliable data pipelines and platforms
• Drive engineering excellence (code quality, CI/CD, testing, observability, incident management)
• Partner with product and analytics to prioritize and deliver high-impact data use cases
• Own production support, incident response, and continuous improvement of data systems
• Establish and scale DataOps practices across development and operations
• Collaborate across AWMT D&A to enable data products that support personalization and analytics
• Hire, develop, and grow engineering talent and future tech leads
• End-to-end delivery and operation of data pipelines and platforms supporting advice products
• Engineering quality, reliability, and standardization across your team
• Talent development for data engineers and tech leads
• Cross-team alignment with product, analytics, and ML engineering
• Improving reliability and observability of production data pipelines
• Driving adoption of consistent pipeline frameworks and engineering standards
• Enabling scalable data foundations for ML and GenAI use cases
• Strengthening team execution, prioritization, and stakeholder trust

Manager, Data Engineering

• Own the data layer of the Fabric platform
• Partner closely with AI Engineers on AI-ready data products
• Design and implement scalable data storage in OneLake
• Build and maintain data ingestion pipelines
• Manage the lifecycle of Fabric data components
• Optimize Lakehouse tables for performance
• Define and operate the on-call model for production data pipelines

Fabric Data Engineer – Workplace Engineering

• 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

• Lead end-to-end platform operations for Teradata and Hadoop environments, ensuring availability, performance, and resilience
• Provide clear ownership and accountability for production services, operational outcomes, and service stability
• Drive incident, problem, and change management, including major incident command and recovery leadership
• Manage operations across shared, multi-tenant platforms, ensuring workload isolation and stability
• Ensure alignment with enterprise risk, compliance, and change management frameworks

Systems Operations Manager – Data Platforms, Teradata, Hadoop

• Design and implement scalable, secure data platforms on Google Cloud using managed services (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
• Build reusable frameworks and tooling (ingestion, transformation, quality, orchestration) that can be adopted by multiple product and domain teams.
• Enable self‑service data consumption and governance by standardizing patterns, templates, and platform capabilities rather than one‑off pipelines.
• Design logical and physical data platform architectures leveraging BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage.
• Define and implement standardized ingestion, transformation, and serving patterns (batch and streaming) as reusable blueprints.
• Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
• Build opinionated data ingestion frameworks (e.g., config‑driven pipelines, connectors, schema handling, error handling) on top of Dataflow, Dataproc, or Composer.
• Develop shared transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
• Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layers in BigQuery and related tools.
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
• Apply security and governance controls: IAM, VPC‑SC, CMEK, row/column‑level security, and policy‑driven access patterns
• Partner with domain data engineers, analytics, and ML teams to onboard use cases onto platform services and frameworks
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples.
• Contribute to platform roadmap, tool selection, and evaluation of new GCP services and open‑source components.

Lead Data Engineer

• Lead the design and implementation of scalable, secure data platforms on Google Cloud using managed services (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer)
• Build reusable frameworks and tooling (ingestion, transformation, quality, orchestration) that can be adopted by multiple product and domain teams
• Develop shared transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines)
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration
• Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies)
• Build opinionated data ingestion frameworks (e.g., config‑driven pipelines, connectors, schema handling, error handling) on top of Dataflow, Dataproc, or Composer
• Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layers in BigQuery and related tools
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards
• Partner with domain data engineers, analytics, and ML teams to onboard use cases onto platform services and frameworks
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples
• Contribute to platform roadmap, tool selection, and evaluation of new GCP services and open‑source components

Lead Data Engineer – GCP Platform Services, Frameworks

• 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.

• 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.

Senior Data Engineer

• 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

• Build and maintain production data pipelines that move performance and product data into the lakehouse with documented schemas, tests, and observability
• Contribute to platform code and Terraform-based infrastructure under the guidance of senior engineers, applying standard patterns and best practices
• Partner with product engineering and domain SMEs to design schema conventions and integrate new data sources cleanly
• Take ownership of components you build, including rotating on-call coverage, responding within SLOs, and contributing runbooks and post-incident learnings
• Anticipate where pipelines can break, build in graceful failure handling, and raise reliability for the components you own

Data Engineer I

• Perform strategic leadership and oversight of data management staff
• Direct performance, security, and scalability of enterprise data environments
• Establish strategic roadmap for database and data warehouse services
• Establish governance structures, policies, and standards for data management and architecture
• Collaborate with senior leadership to align data initiatives with business objectives
• Oversee administration of enterprise databases

Director – Enterprise Data Platforms

• 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

• Lead delivery of high-quality data solutions by partnering with stakeholders and coaching data engineers.
• Own end-to-end data engineering delivery across the project lifecycle.
• Build strong partnerships across the organization to align priorities and deliver data-related goals.
• Design clear, analytics-ready data structures by anticipating downstream analytical needs.
• Evaluate and adopt new technologies and data sources to improve capability and efficiency.
• Automate data ingestion and integration to reliably connect internal and external data sources.
• Document data sources, definitions, and technical solutions to support transparency and reuse.
• Reinforce strong delivery hygiene (version control, code review, automated testing, CI/CD, and operational readiness/monitoring).
• Apply agentic, AI-assisted coding practices to accelerate delivery while maintaining appropriate controls.
• Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access.
• Provide retrieval-ready context (RAG pipelines, vector stores, knowledge bases) when needed.

Manager, Data Engineer

• Own the design, implementation, and operation of production data pipelines that consolidate performance data and product telemetry into our lakehouse
• Lead architecture and infrastructure decisions for our lakehouse platform on AWS, including IaC in Terraform, schema design, and multi-tenant isolation patterns
• Drive data modeling for complex entities like time-series performance data, hierarchical org structures, and multi-source athlete profiles
• Build and document the reusable patterns, runbooks, and Terraform modules that let other product teams self-serve new datasets and integrations
• Raise the bar on platform reliability through observability, on-call rotation ownership, and post-incident learning that reduces toil and cost over time
• Partner with product and engineering teams to evaluate and evolve the platform's technology stack as the business scales into new environments
• Participate in our on-call rotation

• Define the multi-year technical strategy for the process intelligence data platform, aligning platform capabilities with enterprise priorities in process mining, compliance, AI governance, and operational efficiency
• Lead the design and build of a scalable, multi-layered data architecture encompassing storage, semantic and relationship modeling, and AI-ready data layers on cloud infrastructure
• Design and deliver enterprise data lineage capabilities that trace data from source systems through transformation to consumption, and build automated data catalog generation to enable discoverability, impact analysis, and regulatory transparency
• Define and deliver governed data serving APIs that provide secure, performant, and well-documented access to curated datasets, knowledge graph queries, and AI-ready features for downstream consumers (analytics, AI/ML models, compliance tools)
• Define and deliver reusable platform services for state management, task orchestration, checkpointing, event logging, tool invocation traces, replay, evaluation datasets, and human-in-the-loop controls to support reliable production AI workflows
• Drive the technical integration of diverse operational data domains—including work execution data, process and procedure knowledge, workflow and operational systems, and AI agent observability data—into a unified, interconnected data model
• Recruit, develop, and lead a team of data engineers, knowledge graph engineers, ML engineers, and platform engineers; establish engineering standards, delivery practices, and a culture of technical excellence
• Collaborate with Process Intelligence leadership, Risk, Compliance, Operations, and AI Governance stakeholders to define data requirements, access models, and platform SLAs
• Establish data governance frameworks including curated ingestion, validation, end-to-end lineage tracking, access controls, and data quality monitoring to ensure the platform is a trusted, enterprise-approved data source
• Drive cloud-native architecture decisions on GCP and ensure cost-effective, scalable, and secure platform operations
• Ensure the platform provides high-quality, contextualized data that supports knowledge discovery, semantic search, entity extraction, process mining, and GenAI applications

Technical Director – Process Intelligence Data Platform

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight
• Lead architecture reviews and ensure adherence to standards
• Support delivery teams through the product lifecycle.

Enterprise Data Architect

• 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

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