Data Engineer

• Serve as the operational backbone of Kobie's data platform, ensuring the ETL/ELT pipelines, dimensional models, and Snowflake environments that power client reporting and dashboards run reliably, at scale, every day.
• Translate client loyalty program requirements into production-ready data warehouse structures — from source system analysis and dimensional modeling through to the KALC (Kobie Alchemy Loyalty Cloud) platform tables and extracts that downstream reporting and client services teams depend on.
• Diagnosing and resolve data pipeline failures quickly enough that clients and internal stakeholders rarely notice they happened by bringing the debugging fluency and Snowflake depth to contain impact and prevent recurrence.
• Build a data architecture that can absorb change by redesigning Kobie's data mart to support an incoming event-driven, domain-driven application layer without breaking the operational foundation already in production.
• Deliver the data infrastructure that makes AI-powered loyalty products possible. Not by building the models, but by building the reliable, well-governed platform that Kobie's innovation team needs to produce them.
• Leave every pipeline more trustworthy than you found it, through automated testing, audit logging, CI/CD discipline, and documentation that makes the platform easier for the next engineer to own.

Senior Data Engineer

• Design and Implement Snowflake-Native Data Architectures
• Lead the creation and optimization of modern data architectures on Snowflake
• Ensure that the architecture supports high-volume data workloads
• Lead the Development of Complex, End-to-End Data Pipelines Using Native Snowflake Services
• Collaborate with Data Scientists, Analysts, and Other Stakeholders
• Ensure Data Security, Governance, and Compliance Standards Are Met
• Manage role-based access control (RBAC), data sharing, and cross-account governance using Snowflake's native security model

• Lead the end-to-end design, development, deployment, and support of scalable, production-grade data integration and engineering solutions
• Create and maintain technical designs and documentation aligned with business requirements, data architecture, and enterprise standards
• Collaborate with technology leadership to evaluate and implement new tools, technologies, and best practices that improve platform performance and user experience
• Partner with Product Management and business stakeholders to deliver data solutions that meet business objectives and design requirements
• Build strong cross-functional relationships, effectively communicating technical solutions and driving stakeholder alignment
• Mentor junior engineers, guide architectural decisions, and balance technical tradeoffs to deliver reliable and intuitive data products
• Drive operational excellence through monitoring, optimization, troubleshooting, and ongoing support of data platforms and services

Lead Data Engineer

• Lead, mentor, and develop a team of data engineers, setting clear goals, expectations, and growth plans.
• Conduct performance reviews, provide ongoing coaching, and support career development across the team.
• Manage resourcing, capacity planning, and workload balancing across multiple projects and clients.
• Foster a collaborative, inclusive, and high-performing engineering culture.
• Own end-to-end delivery of data engineering initiatives, ensuring quality, timeliness, and alignment with business goals.
• Plan, prioritize, and track team workstreams across client onboarding, migrations, and platform enhancements.
• Remove blockers, manage risks and dependencies, and escalate issues appropriately to leadership.
• Partner with project and program managers to set realistic timelines and commitments.
• Provide technical direction for ETL/ELT development using Azure Data Factory, dbt, Snowflake, Salesforce, and related platforms.
• Review and guide data architecture, models, warehouses, data lakes, and data vault designs for scalability and maintainability.
• Ensure adoption of engineering best practices for integration, transformation, testing, and deployment.
• Make build-versus-buy and tooling recommendations in partnership with the Director.
• Ensure robust data quality frameworks, validation, and monitoring are implemented across pipelines.
• Uphold data accuracy, integrity, security, and compliance, including PHI/PII considerations.
• Establish standards and documentation for data flows, architecture, and transformations.
• Drive resolution of performance, reliability, and cost-efficiency issues across the platform.
• Partner with BI, analytics, product, and business teams to align data solutions with reporting and consumption needs.
• Communicate progress, risks, and technical concepts clearly to both technical and non-technical stakeholders.
• Contribute to roadmap planning and prioritization of data initiatives across the organization.
• Identify and implement improvements to engineering processes, automation, and operational efficiency.
• Evaluate emerging tools, technologies, and frameworks to advance team capability.
• Stay current with industry trends in data engineering, cloud platforms, and big data technologies.
• Perform other related duties as assigned by leadership.
• Directly manages a team of data engineers, including hiring, performance management, and professional development.
• Provides technical and career mentorship and leads cross-functional engineering workstreams.

Senior Manager, Data Engineering

• 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

• The Data Engineer is responsible for data acquisition strategies and integration, scripting and tools, data migrations, conversions, purging, and back-ups.
• The incumbent works with product, financial control, analysts, users, and other stakeholders to understand business requirements and support data architecture to build data acquisition strategies.
• The Data Engineer authors artifacts defining standards and definitions for storing, processing, and moving data including associated processes and business rules.
• Additionally, the Data Engineer maps the details within these their artifacts to business processes, non-functional characteristics, QA criteria and technical enablement.
• He/she constantly thinks through the needs of the business to support efficient and error-free processes.
• Finds trends in datasets and develops workflows and algorithms to make raw data useful to the enterprise.
• Designs and implements standardized data management procedures around data staging, data ingestion, data preparation, data provisioning, and data destruction (scripts, programs, automation, assisted by automation, etc.).
• Ensures quality of technical solutions as data moves across Healthfirst environments.
• Provides insight into the changing data environment, data processing, data storage and utilization requirements for the company and offer suggestions for solutions.
• Ensures managed analytic assets support Healthfirst’s strategic goals by creating and verifying data acquisition requirements and strategy.
• Develops, constructs, tests and maintains architectures.
• Aligns architecture with business requirements and uses programming language and tools.
• Identifies ways to improve data reliability, efficiency, and quality.
• Conducts research for industry and business questions.
• Deploys sophisticated analytics programs, machine learning and statistical methods.
• Prepares data for predictive and prescriptive modeling and finds hidden patterns using data.
• Uses data to discover tasks that can be automated.
• Creates data monitoring capabilities for each business process and works with data consumers on updates.
• Aligns data architecture to Healthfirst solution architecture; contributes to overall solution architecture.
• Helps maintain the integrity and security of the company data.

• build and maintain the cloud infrastructure, data pipelines, and deployment workflows
• turn our team's machine learning models into reliable, production-grade services that support STARS ratings
• work across three key areas: cloud data engineering on Google Cloud Platform, backend API development, and DevOps/container orchestration
• collaborate with data scientists, product owners, and business stakeholders to deliver scalable, well-architected solutions that move insights from development to production

Senior Data Engineer, GCP

• Work with customer subject matter experts and Intralinks project team to identify, define, collate, document, and communicate data migration requirements
• Conduct deep dive data analysis of the customer current state to validate customer requirements and define the scope of the migration
• Strategize and plan the entire legacy system to new Intralinks product migration considering risks, timelines, and potential impacts
• Work with the customer to map legacy data to new Intralinks product
• Analyze and cleanse data where necessary
• Oversee the direct migration of data, which may require unexpected adjustments to the process and schedule
• Provide regular status updates to customer and Intralinks migration teams
• Oversee the quality control process to ensure all data has been migrated and accounted for
• Document everything from the strategies used to the exact migration processes put in place—including documenting any fixes or adjustments made
• Report any issues encountered to Intralinks support
• Conduct regular meetings with the product management team to prioritize and resolve issues that are critical to the success of the migration process
• Develop best practices, processes, and standards to continuously improve the Intralinks data migration process
• Ensure compliance with regulatory requirements and guidelines for all migrated data

Data Migration Specialist

• Design, build, and optimize data ingestion, transformation, and delivery pipelines that support batch, micro‑batch, and streaming workloads.
• Develop ETL/ELT workflows that integrate data from enterprise systems, operational platforms, external sources, and commercial applications.
• Implement scalable data processing patterns that support open lakehouse architectures and medallion‑layered data products.
• Ensure pipelines are reliable, maintainable, cost‑efficient, and aligned with enterprise engineering standards.
• Design data models that support analytics, reporting, and data product consumption across raw, refined, and curated layers.
• Apply medallion architecture principles to ensure data quality, reusability, and consistency across domains.
• Contribute to architectural decisions that balance performance, scalability, governance, and operational simplicity.
• Support integration of ERP and enterprise systems, ensuring data consistency and alignment with business processes.
• Implement data quality checks, validation rules, and monitoring frameworks to ensure accuracy and reliability.
• Apply foundational data governance practices including lineage, metadata management, cataloging, and access controls.
• Collaborate with governance and data product teams to ensure compliance with enterprise standards and regulatory requirements.
• Partner with CSA teams, solution architects, and data product leads to translate business requirements into technical specifications.
• Work closely with customer engineering and analytics teams to support onboarding, adoption, and operationalization of delivered pipelines.
• Participate in Agile delivery processes, providing technical input on sizing, sequencing, and implementation planning.
• Communicate technical concepts clearly to both technical and non‑technical stakeholders.

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC, SFMC, 3rd Party SaaS, and SAP
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Build audience definitions and activation paths for on-site personalization, SFMC campaigns, CRM actions, and downstream systems
• Produce runbooks, onboarding guides, and self-service tooling for marketers, CRM owners, and platform teams.
• Lead cross-functional teams to deliver CDP capabilities.

Customer Data Platform Lead

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC (CRM), SFMC (Marketing Automation), 3rd Party SaaS, and SAP (ERP)
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Define the CDP vision, roadmap, and prioritized use cases aligned to commercial and digital objectives
• Establish canonical data models, event taxonomy, identity graphs, and profile schemas required for personalization and measurement
• Design robust data capture from Sitecore AI DXP, web/mobile events, product catalogs, clinical interactions, and other touchpoints
• Manage Sitecore CDP relationships and third-party integration partners

• 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 Solutions
• Build, maintain, and optimize data pipelines and workflows that support reporting, analytics, and business applications using SQL, Informatica, IICS, and Snowflake.
• Data Processing and Preparation
• Develop and manage data preparation routines for extracts, transformations, and reporting datasets, ensuring reliability and performance.
• Data Modeling and Optimization
• Create and maintain logical and physical data models following normalization principles and best practices.
• Tune queries for optimal performance across Snowflake and Oracle environments.
• Integration and Automation
• Work with various structured and semi-structured data formats (XML, JSON) and leverage AWS cloud frameworks for scalable data integration and processing.
• Data Security and Quality
• Ensure compliance with data security policies and implement best practices for data protection, integrity, and confidentiality.
• Collaboration and Leadership
• Partner with data analysts, developers, and business stakeholders to define data requirements, streamline processes, and improve data accessibility for reporting tools such as Power BI.
• Continuous Improvement
• Recommend and implement improvements to data architecture, workflows, and governance processes.
• Serve as a mentor to junior team members and promote a culture of curiosity and continuous learning.

• 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.

• 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

• 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.

• Own and evolve technical direction for the data engineering platform, including architecture, standards, and long-term platform evolution
• Design and guide the development of scalable, reliable data pipelines and platform capabilities that support analytics and AI use cases
• Contribute to the modernization and evolution of the data platform toward more scalable and managed solutions
• Define and enhance engineering standards, architecture patterns, reusable frameworks, and best practices across the data engineering function
• Improve platform reliability, observability, testing practices, and data quality across production data environments
• Partner with machine learning and related technical teams to support AI platform enablement and help shape MLOps practices where appropriate
• Support broader adoption of AI tools in engineering workflows and day-to-day productivity, including familiarity with tools such as Claude Code and Codex
• Collaborate with Data Engineering, Analytics Engineering, and stakeholders outside the core data organization to align technical solutions with business and platform needs
• Provide selective hands-on technical contribution, particularly in the establishment of foundational services, frameworks, and platform capabilities

Principal Data Engineer

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• 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.

Manager, Data Engineering

• Build and maintain data ingestion, transformation, and delivery pipelines that support batch and micro‑batch workloads.
• Develop ETL/ELT workflows that integrate data from enterprise systems, operational platforms, and external sources.
• Implement scalable data processing patterns aligned with open lakehouse and medallion‑layered architectures.
• Ensure pipelines are reliable, maintainable, and aligned with engineering best practices.
• Contribute to the design of data models across raw, refined, and curated layers.
• Apply medallion architecture principles to support data quality, consistency, and reusability.
• Support integration of ERP and enterprise systems by preparing, transforming, and harmonizing source data.
• Participate in discussions on performance, scalability, and data design decisions.
• Implement data validation rules, quality checks, and monitoring to ensure accuracy and reliability.
• Collaborate with governance and data product teams to ensure compliance with enterprise standards.
• Work with CSA teams, senior engineers, and data product leads to understand requirements and translate them into technical tasks.
• Partner with customer engineering and analytics teams to support onboarding and operationalization of delivered pipelines.
• Participate in Agile ceremonies and contribute to sprint planning, estimation, and delivery.
• Communicate progress, blockers, and technical considerations clearly to teammates and stakeholders.

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