Data Engineer

• Design, build, and maintain trusted risk data products and scalable batch and streaming data pipelines using cloud-native technologies
• Integrate data from risk platforms, control systems, vendor tools, operational applications, and external sources into governed, analytics-ready datasets
• Develop and optimize ETL/ELT workflows for automated data ingestion, transformation, validation, reconciliation, and delivery
• Build and manage data processing workloads in modern data lake and lakehouse environments, including Microsoft Fabric and OneLake
• Implement data quality, monitoring, lineage, and reconciliation processes
• Create curated datasets and analytical outputs for operational reporting, leadership dashboards, trend analysis, risk identification, and decision-making
• Optimize data architectures, schemas, and processing patterns for scalability, performance, resilience, and cost efficiency
• Develop reusable frameworks, engineering standards, CI/CD pipelines, automated testing, and operational monitoring
• Partner with analytics, product, risk, governance, security, and compliance stakeholders on data definitions, metrics, quality standards, and secure data access
• Participate in Agile delivery activities including sprint planning, backlog refinement, design reviews, and continuous improvement initiatives

• Own the design and delivery of core AI/context data capabilities
• Make end-to-end architecture decisions across unstructured ingestion, embeddings, retrieval, semantic layers, and governance
• Design platform architecture from ingestion through parsing/chunking, enrichment, embeddings, vector indexing, and retrieval/serving
• Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Set technical direction for retrieval quality, including query strategies, hybrid search, metadata filtering, and reranking
• Evaluate and select infrastructure, tooling, and cloud services across AWS, Azure, and GCP
• Architect and implement semantic layers for BI and agent reasoning
• Define data and context contracts for AI inputs
• Establish standards for discoverability, documentation, and dataset/index reusability
• Own dbt or semantic-layer tooling strategy across workstreams
• Own platform reliability and performance, including monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems
• Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructure
• Set engineering standards for CI/CD, testing, and retrieval evaluation
• Implement security-by-design patterns including RBAC/ABAC, PII redaction, retention controls, audit logging, and safe agent-tool access
• Partner with Security, Legal, and Compliance on AI access guardrails
• Own governance patterns for sensitive data handling
• Drive technical roadmap decomposition with product, AI, and application stakeholders
• Facilitate architectural decisions and alignment across teams without direct authority
• Mentor engineers through design reviews, code reviews, and documentation
• Potentially lead a small engineering team in the future, including hiring input, technical development, and delivery oversight

Data Architect – Healthcare Insights, Pharmacy

• Own the design and delivery of core AI/context data capabilities
• Design end-to-end platform architecture covering ingestion, parsing/chunking, enrichment, embeddings, vector indexing, and retrieval/serving
• Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Set technical direction for retrieval quality, including query strategies, hybrid search, metadata filtering, and reranking
• Evaluate and select infrastructure, tooling, and cloud services across AWS, Azure, and GCP environments
• Architect and implement semantic layers for BI and agent reasoning
• Define data contracts and context contracts for AI inputs
• Establish standards for discoverability, documentation, and reusability across datasets and indexes
• Own the dbt or semantic layer tooling strategy
• Own platform-level reliability and performance, including monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems
• Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructure
• Set engineering standards for CI/CD, testing, and evaluation
• Implement security-by-design patterns, including RBAC/ABAC, PII redaction, retention controls, audit logging, and safe agent-tool access
• Partner with Security, Legal, and Compliance to define and enforce AI access guardrails
• Own governance patterns for sensitive data handling
• Drive technical roadmap decomposition with product, AI, and application stakeholders
• Facilitate architectural decisions and build alignment across teams without direct authority
• Set best practices and mentor engineers through design reviews, code reviews, and documentation
• Grow into formal people leadership, including hiring input, technical development, and delivery oversight

Data Architect – Healthcare Insights, Revenue Cycle

• Design, build, and lead the evolution of Coates’ next-generation enterprise data platform on AWS
• Architect and evolve a scalable, cost-efficient AWS data platform for enterprise analytics, reporting, and future AI capabilities
• Design and implement production-grade batch and near-real-time data pipelines and processing frameworks
• Define enterprise standards for data architecture, modeling, observability, reliability, and platform performance
• Lead 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 governed data products through scalable semantic layers, curated datasets, and transformation standards
• Provide hands-on technical leadership, mentoring, and architectural direction for data engineers and cross-functional stakeholders

Data Engineering Lead

• Design and provide level-of-effort estimates for changes to Insights applications
• Develop high-quality modifications to application source code, processes, and architecture
• Deliver fully tested code before production deployment when appropriate
• Troubleshoot application bugs and issues
• Coordinate with engineers to ensure comprehensive platform changes meet business needs
• Recommend and implement enhancements to streamline processes and improve platform performance, availability, and functionality
• Assist with project plans and capability roadmaps
• Maintain current documentation of system and solution designs
• Provide training to internal functional SMEs and stakeholders
• Provide technical leadership to engineers working on data solutions
• Translate business and analytical requirements into reliable, scalable, production-grade data solutions
• Design, build, deploy, and operate data pipelines, services, and applications
• Define technical standards, architectural patterns, and operational practices
• Design data workflows, APIs, processing logic, and supporting infrastructure at scale
• Work closely with a Solution Architect and provide technical mentorship to Data Engineers

Senior Lead 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
• Gather requirements, build pipelines, and create dashboards hands-on
• Report to the Director of Business Intelligence

Senior Data Engineer

• Establish the strategic direction for JLL's AI/BI ecosystem and define semantic and context layer capabilities
• Evangelize consistent data consumption methodologies and drive business line adoption
• Partner with business units, product leaders, product teams, platform teams, and engineering teams
• Design and establish ontologies, knowledge graphs, semantic models, analytical data models, and dimensional designs
• Lead cloud-native data architecture initiatives using the Databricks technology stack on Azure or AWS
• Build and deploy AI-first data consumption solutions through MCP and Text-to-SQL agents using Databricks Genie or JLL-built SQL query agents
• Drive architecture for agentic AI and intelligent data consumption use cases with evaluation frameworks for consistency and reliability
• Design and build agentic solutions integrating unstructured data through RAG agents and structured data through SQL query agents
• Mentor technical leads and senior engineers
• Lead architecture reviews, technical strategy discussions, and data-platform investment decisions
• Champion AI-Harness adoption for AI-powered development lifecycle capabilities
• Establish enterprise-wide standards, reusable patterns, technical guardrails, and measurable success metrics

Principal Data Architect

• Design, build, and optimize end-to-end data pipelines across ingestion, transformation, modeling, and distribution layers
• Validate, document, and communicate changes to data pipelines and models prior to release
• Translate business requirements into scalable and maintainable technical solutions
• Contribute to solution design decisions within established enterprise architectural patterns
• Ensure data reliability, quality, and accessibility across systems and data products, including monitoring SLAs where appropriate
• Troubleshoot and resolve complex data and system issues, including root cause analysis
• Optimize performance, scalability, and cost efficiency of pipelines and data products
• Leverage AI-assisted engineering tools to accelerate development, automate tasks, and improve documentation, with validation of outputs
• Partner with team members and stakeholders to address customer needs, deliver measurable business value, and align with product objectives
• Collaborate with engineers through design collaboration and code reviews
• Operate with autonomy and contribute to the evolution of RLI's data platform
• Report to a Data Engineering Manager

Senior Data Engineer – Snowflake

• Understand and translate business needs into data models and data architecture supporting long-term solutions
• Work with the Application Development team to implement data strategies, build data flows, and develop data models
• Create and maintain conceptual, logical, and physical data models using best practices to ensure high data quality and reduced redundancy, with corresponding metadata
• Optimise and update logical and physical data models for new and existing projects
• Develop best practices for standard naming conventions and coding practices
• Maintain versioned documentation and models for consistent delivery and understanding between client and development team
• Recommend opportunities to reuse data models in new environments
• Reverse-engineer physical data models from databases and SQL scripts
• Evaluate data models and physical databases for variances and discrepancies
• Oversee organizational data modelling, lead high-impact initiatives, ensure alignment with organizational goals, and provide executive guidance

Data Architect

• Design and implement data strategies aligned with business goals, assessing current infrastructure and identifying optimization opportunities
• Collaborate with clients and stakeholders to translate business needs into actionable data initiatives and insights
• Define and enforce data governance frameworks, ensuring quality, security, and regulatory compliance
• Recommend and oversee adoption of data technologies and platforms, supporting scalable architectures and advanced analytics including AI, ML, and predictive models
• Partner with teams to interpret data, uncover trends, and foster data-driven decision-making
• Promote data literacy, provide training, and support change management for adoption of new data practices and technologies