Jll
Real Estate
DataEngineerII
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer II at Jll. Skills: Data Engineering, Python, SQL, Agentic AI. Design, build, and maintain data pipelines. Develop data services and APIs”
Industry & Context.
What They're Looking For.
Must Have
Python, SQL, Data pipelines on a cloud data platform (Databricks, Azure Synapse, or equivalent), PySpark or equivalent distributed data processing framework, Data orchestration tools (Azure Data Factory, Airflow, Databricks Workflows, or similar), Delta Lake, lakehouse architecture, or similar open table formats, AI/ML integration, LLM APIs, or agent frameworks (LangGraph, LangChain, or equivalent), Python web frameworks (FastAPI preferred) for building data services and APIs, AI-powered development tools (Cursor AI, GitHub Copilot, or similar), Git version control, Microsoft Azure cloud platform
Nice to Have
Databricks Genie or natural language to SQL query platforms, Vector databases (Qdrant, PgVector, ChromaDB), LangGraph multi-agent orchestration, Event-driven patterns (Azure Service Bus, Dapr), Azure cloud services (Data Lake, Azure Data Factory, Key Vault, Blob Storage), Distributed task processing (Celery, Redis), Containerization (Docker) and Kubernetes, Data governance frameworks, data cataloging tools, or data quality platforms, Observability tools (Datadog, LangSmith)
What You'll Do.
and maintain data pipelines
Develop data services and APIs
Write and optimize SQL
Implement pipeline monitoring and data quality checks
Manage data workflows using orchestration tools
Build AI agents to automate data engineering tasks
Develop agentic workflows
Implement LLM-powered natural language to data query capabilities
Collaborate with AI engineers to deploy RAG pipelines
Build and maintain data models and lakehouse architecture
Implement data access patterns and caching
Develop event-driven data workflows
Assist in distributed task processing
Contribute to CI/CD pipelines and infrastructure-as-code
Write unit and integration tests
Participate in code reviews
Implement structured logging and observability
Follow data governance
and compliance practices
Collaborate with senior engineers and product managers
Document data models and pipeline design
Participate in GenAI knowledge-sharing sessions
Upskill in emerging Agentic AI frameworks
Take ownership of data domains and pipeline components
How You'll Work.
Team & Collaboration
Collaborate with senior data engineers, AI engineers, and product managers in an Agile environment; Work collaboratively in a team environment
Communication Scope
Good communication skills
Full Job Description
**JLL empowers you to shape a brighter way**. Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. ## **About the Role** We are looking for a Data Engineer II with hands-on data engineering experience and a growing passion for Agentic AI to join our Azara Data & AI Engineering team at JLL Technologies. You will build and maintain data pipelines, transformation workflows, and data services that power Azara, our AI-driven data intelligence platform for commercial real estate — while actively developing AI-augmented and agentic capabilities to make those workflows smarter, self-healing, and more autonomous. This role is ideal for a data engineer who wants to go beyond traditional pipelines and apply Agentic AI to real-world data engineering challenges at enterprise scale. ## **Key Responsibilities** ### **Data Engineering & Pipeline Development** * Design, build, and maintain scalable data ingestion, transformation, and serving pipelines using Python and PySpark on Databricks * Develop data services and APIs (FastAPI) that expose curated data assets to downstream applications and AI services * Write and optimize SQL for data transformation, aggregation, and quality validation across large-scale datasets * Implement pipeline monitoring, alerting, and data quality checks to ensure reliability and SLA compliance * Manage data workflows using orchestration tools (Azure Data Factory, Airflow, or Databricks Workflows) ### **Agentic AI Integration** * Build AI agents that automate data engineering tasks such as self-
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