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

• Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
• Improve metadata-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
• Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
• Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
• Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
• Support cross-domain analytics initiatives by transforming source-refined data into trusted, reusable, business-aligned derivative data products.
• Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
• Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
• Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment-aware platform delivery processes.
• Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
• Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
• Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI-ready data product frameworks.

Senior Artificial Intelligence Data Engineer

• Develop and iterate data engineering pipelines, dashboards, reports, and insights for internal partners and external customers
• Provide expertise on data engineering tools, visualization tools, concepts, and best practices
• Partner with Analytics, Data Science, and Technology to define best practices, policies, and procedures for data usage
• Translate complex data engineering requirements from concept through implementation
• Collaborate with Information Technology and analytics team members to advance self-service analytics capabilities
• Engage hands‑on to gather requirements, build pipelines, and create dashboards

Senior Data Engineer

• 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

• Build and own the AI context platform
• Design and implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving
• Improve retrieval quality in partnership with AI engineers
• Define and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently
• Own reliability and performance: monitoring, alerting, SLAs/SLOs, incident response
• Implement security-by-design and lead through influence

Senior Data Engineer, AI & Context Platform, Healthcare Insights, Rev Cycle

• Design, build, and maintain Power BI datasets, semantic models, and reports used across multiple client engagements
• Develop and optimize data models and DAX calculations to support scalable, high‑performance analytics and dashboards
• Prepare, transform, and shape data for analytics using SQL, Power BI (Power Query), and Azure‑based data sources
• Coordinate and support Power BI dataset refreshes, troubleshoot failures, and resolve performance or data issues
• Perform data quality assurance to validate accuracy, completeness, and reasonableness of data prior to analytics and reporting use
• Review incoming client data and develop mapping logic and documentation to support analytics‑ready SQL tables and Power BI datasets
• Collaborate closely with stakeholders to translate reporting and business requirements into clear, consumable Power BI dashboards
• Support Azure‑based data environments that feed Power BI solutions, including SQL databases, Azure Data Factory pipelines, and SFTP integrations
• Enable secure client data ingestion by supporting SFTP access, file transfers, and troubleshooting connectivity and ingestion issues
• Work with healthcare data containing PHI, ensuring compliance with security, privacy, and data governance standards throughout the analytics lifecycle
• Collaborate with internal team members and consultants to resolve data and reporting issues, prioritize requests, and support multiple concurrent client engagements

Data Operations Engineer

• Build and contribute to the AI context platform
• Implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving
• Build and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers
• Deliver semantic and governed data products
• Implement semantic layers (metrics/entities) that power BI and agent reasoning consistently
• Ensure datasets and indexes are documented and reusable
• Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response

Data Engineer – AI, Agents, Context, Clinical – Senior Associate

• Build and contribute to the AI context platform
• Implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving
• Build and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers
• Deliver semantic and governed data products
• Implement semantic layers (metrics/entities) that power BI and agent reasoning consistently
• Ensure datasets and indexes are documented and reusable
• Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response
• Contribute to cost and latency optimization across Snowflake and vector infrastructure
• Apply security-by-design patterns: RBAC/ABAC, PII redaction, retention controls, and audit logging

Data Engineer – AI, Agents, Context – Revenue Cycle (Associate)

• 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.

• Collaborate with senior data engineers to translate complex business requirements and ambiguous problem statements into clear, robust, and scalable technical designs and data models (e.g., dimensional modeling, star schemas), and independently drive the implementation of these designs.
• Design, build, and deploy high-volume data transformation logic using highly optimized PySpark.
• Contribute significantly to the design and improvement of CI/CD pipelines in Azure DevOps/Git.
• Deeply understand and connect to various source systems, demonstrating proficiency in managing data persistence and query performance across diverse technologies like SQL Server, Neo4j, and CosmosDB.
• Proactively implement and maintain advanced data quality frameworks (e.g., Delta Live Tables, Great Expectations) and monitoring solutions to ensure data reliability for mission-critical applications.
• Serve as a go-to technical resource for peers, conducting technical code reviews and informally mentoring Associate Data Engineers on PySpark and Databricks best practices.

• 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 scalable ERP data pipelines for platforms such as Workday and enterprise systems
• Own end‑to‑end lifecycle of ERP data pipelines (ingestion → transformation → delivery)
• Support data conversion and migration initiatives (e.g., M&A, system integrations)
• Ensure pipeline reliability, recoverability, and stable production operations
• Troubleshoot pipeline failures and resolve root causes of data defects and operational issues
• Partner with business analysts and stakeholders to translate requirements into data solutions
• Implement and enforce SDLC best practices (version control, code reviews, CI/CD, release management)
• Develop and maintain data quality controls (freshness, completeness, anomaly detection, schema management)
• Detect and manage schema drift and downstream data issues proactively
• Build and maintain monitoring, alerting, and observability for data pipelines
• Optimize performance, scalability, and cost of data workloads
• Track operational KPIs (e.g., pipeline success rates, SLAs, incident trends, recovery times)
• Create runbooks, documentation, and audit‑ready operational processes
• Collaborate with data governance, security, and compliance teams on controls and data access
• Support analytics, reporting, and integration teams with reliable, well‑governed datasets
• Communicate effectively with technical and non‑technical stakeholders and escalate risks early

Senior ERP Data Engineer

• Specialize in turning raw data into business-ready products within GCP
• Design and optimize data models, marts, and semantic layers in BigQuery
• Enable analytics, BI, and ML use cases across the enterprise
• Support downstream systems and APIs that deliver trusted data for operational and AI-driven processes
• Build the pipelines, products, and platforms that power the next generation of digital distribution

Data Engineer – Data Products, Delivery

• Design and develop reusable, parameter-driven ingestion and transformation pipelines
• Build and maintain medallion architecture solutions
• Develop performant ELT workflows
• Create and optimize PySpark notebooks and distributed processing jobs
• Design dimensional data models
• Implement data vault patterns
• Optimize distributed SQL workloads
• Implement CI/CD processes
• Build monitoring, logging, and auditing solutions
• Lead or contribute to cloud modernization initiatives

Enterprise Data Warehouse ETL/Data Engineer

• Lead the Data Engineering and Data Science Engineering function for IAA, setting technical vision, delivery strategy, and operating rhythm
• Build and evolve scalable data platforms, BI architecture, and ML-enablement capabilities using the Azure data and analytics stack
• Drive strategy and execution across Microsoft Fabric, Synapse, Power BI, Azure BI technologies, and modern cloud data platforms
• Partner with business and functional leaders to solve high-value problems across Operations, Sales, Marketing, Product, and other key areas
• Mentor, coach, and grow data engineering and data science talent while raising the technical bar across the team

Director of Data Engineering, Data Science

• Architect and evolve a scalable, cost-efficient AWS data platform
• Design and implement reliable, production-grade data pipelines and processing frameworks
• Define enterprise standards for data architecture, modeling, observability, reliability, and platform performance
• Lead foundational technical decisions across tooling, infrastructure, data design, scalability, and operational efficiency
• Implement and maintain infrastructure-as-code, CI/CD pipelines, and automated deployment practices
• Enable high-quality, accessible, and governed data products

Tech Lead, Data Engineering

• Lead the Enterprise Platform data engineering function—defining the roadmap, operating model, and standards that implement JLL’s company data strategy and data quality programs at enterprise scale.
• Align team execution with JLL’s enterprise data strategy while delivering the platform capabilities that all business units depend on.
• Define and drive the Enterprise Platform data strategy—implementing JLL’s company data strategy and consolidating siloed data sources into a unified, governed, and scalable data architecture.
• Provide technical leadership across the Enterprise Platform Data Engineering team and related initiatives, setting architectural standards, design patterns, and best practices.
• Architect sophisticated integration strategies for structured and unstructured data sources.
• Design and implement enterprise-grade data integration frameworks.

Senior Manager, Data Engineering

• 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

• Drive solution design for complex, cross-functional data and AI problems — from initial discovery through to technical blueprint
• Define and communicate architecture decisions, trade-offs, and delivery approaches to both technical and non-technical audiences
• Design scalable, modular systems that balance the need for speed with enterprise standards for reliability, security, and maintainability
• Participate in architecture reviews and Critical Design Reviews (CDRs), ensuring alignment with enterprise patterns and platform standards
• Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs
• Design and deliver working prototypes for complex data and AI problems within compressed timeframes, often days to weeks
• Bring genuine curiosity to every engagement — deeply understanding the business case, problem context, and constraints before converging on a solution
• Balance speed of delivery with enterprise standards — your prototypes are production-ready, not throwaway
• Continuously iterate on solutions based on direct feedback from product managers, program leads, and end users
• Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end
• Develop and maintain agent skills — discrete, reusable capabilities that compose into larger agentic pipelines
• Implement and extend MCP (Model Context Protocol) servers and clients to connect AI agents with enterprise tools, APIs, and data sources
• Build agent orchestration layers using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel
• Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production
• Stay current with the rapidly evolving agentic AI landscape and proactively introduce new techniques and tooling to the team
• Build and deploy AI-powered features and pipelines that automate workflows, surface insights, and enhance decision-making
• Design and implement scalable data pipelines, APIs, and backend services that serve both internal tools and customer-facing products
• Integrate LLMs, RAG systems, and ML models into production data workflows
• Own data modeling, transformation, and quality across the solutions you deliver
• Embed directly with product, program, and engineering teams to co-define problems and co-deliver solutions
• Contribute to technical direction and help build alignment across teams through strong communication and collaboration
• Communicate complex technical concepts clearly to non-technical business stakeholders — in writing, in meetings, and in presentations
• Support and mentor junior engineers, sharing patterns and practices for agentic development, prompt design, and rapid delivery
• Foster a collaborative, low-ego team culture where speed and quality go hand in hand.

• 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

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Senior Staff AI Data Engineer

Senior Artificial Intelligence Data Engineer

Senior Data Engineer

Senior Data Engineer, GCP

Senior Data Engineer, AI & Context Platform, Healthcare Insights, Rev Cycle

Data Operations Engineer

Data Engineer – AI, Agents, Context, Clinical – Senior Associate

Data Engineer – AI, Agents, Context – Revenue Cycle (Associate)

Senior Data Engineer

Data Engineer

Senior Data Engineer

Senior ERP Data Engineer

Data Engineer – Data Products, Delivery

Enterprise Data Warehouse ETL/Data Engineer

Director of Data Engineering, Data Science

Tech Lead, Data Engineering

Senior Manager, Data Engineering

Data Engineer

Senior Data Engineer

Data Engineer