Data Engineer

• Lead the design and architecture of our most critical data pipelines and data platform components
• Set technical standards and best practices that elevate the entire data team
• Mentor and support junior and mid-level engineers through code reviews, pair programming, and knowledge sharing
• Own the health, scalability, and reliability of our orchestration infrastructure
• Partner with engineering, analytics, and leadership to shape the roadmap for our data platform
• Evaluate and introduce new tools and technologies to keep our stack modern and effective
• Ensure compliance and confidentiality of Personal Health Information across all data

Senior 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

• Own end-to-end architecture for the Databricks-on-Azure lakehouse platform
• Define and scale medallion architecture standards across domains
• Establish platform standards and reusable patterns to accelerate Databricks adoption
• Design and implement secure, compliant data access frameworks
• Lead CI/CD and DevOps patterns for Databricks deployments
• Optimize performance and cost across Delta Lake pipelines
• Build reusable ingestion frameworks for structured and streaming data
• Partner with Data Governance, Security, SRE, and Legal teams to align platform design
• Provide architectural guidance on PHI/PII data handling
• Mentor engineers and architects; establish design reviews and documentation practices

Senior Data Architect, Databricks

• Build, maintain, and optimize data pipelines across a variety of source systems.
• Support and improve our core data warehouse infrastructure, primarily in Snowflake, with some legacy warehouse environments such as Redshift.
• Develop and maintain transformation logic, models, and reusable data assets using tools such as dbt.
• Build new warehouse functionality, curated data models, marts, and tables that support reporting, analytics, operations, and stakeholder decision-making.
• Support BI and reporting workflows across Looker and Domo, partnering with analysts and business teams to ensure trusted, consistent metrics.
• Manage and troubleshoot existing data pipelines, jobs, connectors, data shares, SFTP connections, APIs, and native integrations.
• Write and maintain production-quality SQL, Python scripts, and transformation workflows.
• Partner with analysts and business stakeholders to understand data needs and translate them into reliable, scalable data solutions.
• Help ensure our data is accurate, timely, well-documented, and trusted by the teams that rely on it.
• Explore and adopt AI-assisted engineering tools such as Claude Code, Cursor, and other agentic AI frameworks to improve development velocity, documentation, testing, data quality, and operational efficiency.
• Support warehouse migrations, platform consolidation, and modernization efforts as the company continues to scale.
• Collaborate with cross-functional teams across marketing, sales, operations, finance, product, technology, and mortgage operations.
• Contribute to data quality monitoring, observability, governance, and process improvements.

• Design and develop AI-enabled analytics solutions supporting enterprise reporting, forecasting, operational insights, and intelligent decision-making.
• Support enterprise AI initiatives including: Generative AI integrations, AI-assisted analytics, Natural language reporting, Predictive analytics, Recommendation and insight generation capabilities.
• Develop reusable analytics frameworks and scalable data products supporting enterprise reporting and AI use cases.
• Identify opportunities to leverage AI capabilities to improve reporting development efficiency and business insight generation.
• Design and implement AI-driven solutions that proactively identify data anomalies, schema changes, integration failures, reporting inconsistencies, and data quality issues across enterprise platforms.
• Develop intelligent monitoring and alerting frameworks that detect abnormal data patterns, missing data, volume fluctuations, and KPI deviations before impacting business operations.
• Create AI-enabled solutions that proactively analyze upstream system and schema changes to identify potential downstream reporting and integration impacts.
• Collaborate with governance and architecture teams to establish standards for responsible AI usage within analytics and reporting ecosystems.

AI Data Engineer

• 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

• Job Title: Sr. Data Engineer - MS Fabric, ETL/ELT, Data Modelling, and Cloud platforms
• Location: Lansing, MI (Onsite-Hybrid)
• Overall, 10 + years of experience and Azure Certification is MUST.
• We are looking for a hands-on Data Engineer with strong experience in MS Fabric, ETL/ELT, Data Modelling, and Cloud platforms.
• We are looking for someone who can work independently.
• Candidates with experience working in or leading junior teams would be a good fit.
• We mainly need someone who can communicate well, present their experience confidently, and perform strongly.

• Establish the security architecture and governance framework for industrial AI and analytics
• Define and govern enterprise-wide industrial security architecture standards
• Develop strategies for secure remote access and vendor connectivity
• Define approved architectures for industrial data collection and AI model deployment
• Ensure operational data can be securely leveraged for predictive maintenance and advanced analytics

Data Engineer, P5

• 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.

• Designs, develops and optimizes highly complex, specialized data warehouses
• Analyzes business intelligence data and makes recommendations for growth and integration
• Works with IT and business customers to develop requirements for data warehouse architecture
• Provides support and guidance to ensure architecture aligns with BI application needs
• Leads and consults on implementation of data warehouses and ensures data integrity

Senior Data Architect – Retail Architecture

• Architecting, creating and maintaining data pipelines
• Assist with renovating the data management infrastructure to drive automation in data integration and management
• Work in partnership with data science teams and with business analysts in refining their data requirements
• Train counterparts across the organization in data pipelining and preparation techniques
• Work with data governance teams and participate in vetting and promoting content created in the business
• Demonstrated ability to communicate complex results to technical and non-technical audiences
• Work effectively in teams as well as independently across multiple tasks
• Strategic, intellectually curious thinker with focus on outcomes

• 8–10 years in Data Engineering and Data Analysis.
• Strong hands-on experience in Informatica PowerCenter/IDQ for ETL design, development, and optimization.
• Advanced skills in PySpark for large-scale data processing, transformation, and analytics.
• Solid working knowledge of Hadoop technologies (HDFS, Hive, Sqoop, MapReduce).
• Proficiency in Python and Kafka for streaming and batch data pipelines.
• Strong understanding of database concepts, data design, data modeling, and ETL workflows.
• Experience in analyzing, designing, and coding ETL programs including data extraction, ingestion, quality checks, normalization, and loading.
• Hands-on experience with Agile methodology and Jira for project delivery.
• Proven ability in client-facing roles with strong communication and leadership skills to coordinate across SDLC.
• Exposure to AWS data components and analytics.
• Familiarity with machine learning models and AI concepts.
• Experience with data modeling tools such as Erwin.

• Define and drive global master data architecture strategy
• Lead the architectural design and execution for core data domains
• Analyze the SAP Simplification List to identify deprecated objects and mandatory conversions
• Establish and maintain standard data models for cross-functional data integrity
• Define the methodology and standards for data cleansing and migration readiness
• Act as the primary technical advisor to the VP, Data Intelligence & AI

Master Data Architect

• 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

• 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

• Design, develop, and optimize scalable data pipelines and cloud-based data solutions
• Collaborate with analytics, product, architecture, governance, and business teams
• Conduct design and innovation of data extraction, ingestion and transformation processes
• Develop and design complex data architecture for high-volume data sets
• Advocate for and implement data security and privacy measures
• Solve complex problems and participate in strategic planning sessions

• Lead the strategy and execution of complex and large Data and Analytics portfolio.
• Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
• Effectively communicate strategy, execution progress, and outcomes to diverse stakeholders and promote data capabilities through thought leadership and presentations.
• Responsible for implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
• Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
• Ensure semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
• Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
• Identify and champion AI augmented productivity improvements across the end-to-end data management lifecycle.
• Stay current with emerging trends in Agentic AI and data engineering and lead proof-of-concepts and early pilots for emerging data and AI augmented technologies to accelerate speed to market.
• Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices.
• Effectively manage the budget and financials for the portfolio.

AVP, AI Data Engineering, Customer Data Ecosystem

• Define Industrial Data Architecture for AI
• Design end-to-end data flows from: Edge systems → cloud → AI pipelines → edge inference
• Design Feature Pipelines and Delivery for AI Models
• Integrate Semantic Model with Data Pipelines
• Enable Scalable AI Model Integration
• Design for Multi-Tenant and Product Use Cases

Principal Industrial AI Data Architect

• 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.

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