What This Job Involves We are seeking an experienced Senior Data Engineer with full‑stack capabilities to join our team. In this role, you will be responsible for designing, building, and scaling a core data platform that powers data‑driven insights across JLL. You will consolidate siloed systems into a unified data layer, serving as the single source of truth for stakeholders across the business. The ideal candidate is a skilled data architect and software engineer who can translate user requirements into robust data products and applications. What You’ll Get To Do As a Senior Data Engineer, you will have ownership over critical components of our data ecosystem. Your “customers” are internal and external stakeholders—including brokers, analysts, application teams, and data scientists—and your mission is to provide them with the data and tools needed to generate market‑leading insights. You will partner with Research, Product, and Engineering teams to build a platform that can keep pace with shifting market priorities. This is an opportunity to make a significant impact by building the foundational data layer for intelligent applications, Agentic AI workflows, and LLM‑powered assistants. Key Responsibilities Platform Architecture and Design: Design, build, and scale the data platform, consolidating regional and country‑specific systems into a unified, governed data layer. Data Integration: Integrate structured and unstructured data sources into the platform to enable advanced analytics, model building, and insight generation. Application Development: Build features and full‑stack applications that bridge structured and unstructured data for downstream consumption, enabling smarter search, contextual recommendations, and automated report generation. Integration and Consumer Enablement: Build critical integration components, including robust APIs, integration patterns, and a Model Context Protocol (MCP), to enable applications, Agentic AI, and analytics users to easily consume data. ML Model Integration: Integrate machine learning models into production workflows, ensuring they can be reliably triggered, monitored, and consumed by downstream applications. AI Enablement: Design semantic layers and knowledge graphs to make commercial real‑estate data discoverable and consumable by both stakeholders and AI/ML systems. Technical Excellence: Lead technical solutioning across the full project lifecycle, from discovery and architecture through deployment and iteration. DevOps and Monitoring: Deploy and monitor data products on cloud platforms with built‑in observability, telemetry, data lineage, and auditability to ensure outputs are traceable and compliant. Collaboration: Partner closely with Business, Product, and Engineering teams to translate stakeholder needs into data products with a rapid, iterative turnaround. Who You Are You are a seasoned engineer with a passion for building robust data platforms and the applications that consume them. You think in terms of scalable systems and enjoy the challenge of designing services that serve as the backbone for data‑intensive applications and analytics. You are motivated by an iterative, fast‑paced environment where priorities shift with market conditions. You have a strong sense of ownership and take pride in the quality and reliability of the data infrastructure you build. Qualifications And Skills Required Qualifications 4–6 years of experience in data engineering and Big Data development, with a focus on scalable, fault‑tolerant architectures. 2–3 years of hands‑on experience with cloud platforms such as Azure or AWS (e.g., Databricks, Azure Data Factory, Synapse). Strong proficiency in a server‑side programming language such as Python, Java, or Scala, with specific experience in PySpark/Spark for distributed data processing. Solid software engineering experience in backend development, including Java and Spring Boot. Proven data modeling and architecture skills, with an understanding of how data structures affect retrieval and analytics. A fundamental understanding of machine learning concepts and how models are trained and deployed. Experience working with SQL (e.g., Azure SQL), NoSQL (e.g., Cosmos DB, MongoDB), and AI‑centric databases such as vector or knowledge/graph databases. Demonstrated ability to write optimized, scalable SQL and work with large datasets. Preferred Qualifications Bachelor’s degree in Computer Science, Engineering, or a related field. Experience with modern frontend frameworks such as React, Angular, or Vue.js. Experience designing and implementing semantic layers or knowledge graphs. Hands‑on experience with streaming tools like Kafka or Spark Streaming. Strong understanding of DevOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes). Exposure to LLM‑driven workflows, prompt engineering, or orchestration tools (e.g., LangChain, LlamaIndex, CrewAI). Familiarity with AI‑powered development tools (e.g., Codex, Claude, Cursor) as part of an AI‑augmented software development lifecycle. Key attributes Problem‑Solving Skills: Excellent analytical and systems‑thinking abilities. Ownership & Drive: A proactive, self‑motivated individual with a strong sense of ownership for the platform and products you build. Collaboration: A team‑first attitude with a track record of cross‑functional collaboration with business stakeholders, product managers, and distributed engineering teams. Communication: Strong verbal and written communication skills, with an ability to articulate complex technical concepts clearly. Adaptability & Speed: The ability to work at a fast pace and translate business needs into delivered data products with a quick turnaround. #J-18808-Ljbffr