JLL supports the Whole You, personally and professionally.
Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology to our clients. We are committed to hiring the best, most talented people in our industry; and we support them through professional growth, flexibility, and personalized benefits to manage life in and outside of work. Whether you’ve got deep experience in commercial real estate, skilled trades, and technology, or you’re looking to apply your relevant experience to a new industry, we empower you to shape a brighter way forward so you can thrive professionally and personally.
About the RoleWe 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 ResponsibilitiesData Engineering & Pipeline DevelopmentDesign, 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 IntegrationBuild AI agents that automate data engineering tasks such as self-healing pipelines, anomaly detection, and automated data quality remediation
Develop agentic workflows using LangGraph or LangChain that integrate with data platforms and enterprise data sources
Implement LLM-powered natural language to data query capabilities (e.g., Databricks Genie-style interactions) for data access layers
Integrate LLM APIs (Azure OpenAI) into data services for intelligent data enrichment, classification, and summarization
Collaborate with AI engineers to deploy RAG pipelines that leverage data assets as knowledge sources for agent workflows
Data Platform & Cloud InfrastructureBuild and maintain data models, Delta Lake tables, and lakehouse architecture components on Databricks and Azure
Implement data access patterns, caching (Redis), and partitioning strategies for efficient data serving
Develop event-driven data workflows using Azure Service Bus and Dapr for real-time pipeline triggers
Assist in distributed task processing (Celery) for scalable, async data workloads
Contribute to CI/CD pipelines and infrastructure-as-code for data platform components
Quality & Engineering PracticesWrite unit tests and integration tests (pytest) for pipeline logic, data transformations, and AI-integrated components
Participate in code reviews with attention to data quality, pipeline reliability, and AI-specific concerns (hallucination, cost, prompt safety)
Implement structured logging and observability for pipeline health and AI workflow performance
Follow data governance, security, and compliance practices for enterprise data handling
Collaboration & GrowthCollaborate with senior data engineers, AI engineers, and product managers in an Agile environment
Document data models, pipeline design decisions, and agentic workflow patterns
Participate in GenAI knowledge-sharing sessions and actively upskill in emerging Agentic AI frameworks and techniques
Progressively take ownership of data domains and pipeline components with increasing independence
Required Qualifications3–5 years of professional data engineering experience with strong proficiency in Python and SQL
Hands-on experience building and maintaining data pipelines on a cloud data platform (Databricks, Azure Synapse, or equivalent)
Working experience with PySpark or equivalent distributed data processing framework
Experience with data orchestration tools (Azure Data Factory, Airflow, Databricks Workflows, or similar)
Familiarity with Delta Lake, lakehouse architecture, or similar open table formats
1+ year of hands-on experience with AI/ML integration, LLM APIs, or agent frameworks (LangGraph, LangChain, or equivalent)
Experience with Python web frameworks (FastAPI preferred) for building data services and APIs
Experience with AI-powered development tools (Cursor AI, GitHub Copilot, or similar) for AI-augmented development across the SDLC
Familiarity with Git version control and collaborative development workflows
Basic understanding of Microsoft Azure cloud platform
Preferred QualificationsExperience with Databricks Genie or natural language to SQL query platforms
Familiarity with vector databases (Qdrant, PgVector, ChromaDB) for RAG pipeline integration
Exposure to LangGraph multi-agent orchestration for data automation workflows
Experience with event-driven patterns (Azure Service Bus, Dapr) and real-time streaming
Familiarity with Azure cloud services (Data Lake, Azure Data Factory, Key Vault, Blob Storage)
Experience with distributed task processing (Celery, Redis) for async data workloads
Familiarity with containerization (Docker) and Kubernetes for data service deployment
Exposure to data governance frameworks, data cataloging tools, or data quality platforms
Familiarity with observability tools (Datadog, LangSmith) for pipeline and AI workflow monitoring
Technical Skills & CompetenciesData EngineeringLanguages: Python, SQL, PySpark
Platforms: Databricks (Delta Lake, Workflows, Genie)
Cloud: Azure (Data Lake, ADF, Blob Storage, Key Vault)
Orchestration: Azure Data Factory, Databricks Workflows, Airflow (awareness)
Patterns: ELT/ETL, lakehouse architecture, streaming and batch pipelines
Agentic AI & IntegrationAgent Frameworks: LangGraph (primary), LangChain
LLM Providers: Azure OpenAI, OpenAI
Techniques: RAG for data, NL-to-SQL, prompt engineering, function calling
Vector Databases: Qdrant, PgVector, or ChromaDB (awareness)
Core EngineeringFrameworks: FastAPI, Pydantic, Celery
Databases: PostgreSQL, Redis
Event-Driven: Azure Service Bus, Dapr (awareness)
DevOps: Git, CI/CD, Docker basics
Experience & EducationBachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent professional experience
3–5 years of professional data engineering experience with demonstrable pipeline and AI integration skills
Good communication skills and ability to work collaboratively in a team environment
Demonstrated curiosity and passion for applying AI to data engineering challenges
Familiarity with Agile methodologies and principles
What We Can Do for YouAt JLL, we make sure that you become the best version of yourself by helping you realise your full potential in an entrepreneurial and inclusive work environment. If you have a passion for learning and adopting new technologies, JLL will continuously provide you with platforms to enrich your technical expertise. We will empower your ambitions through our dedicated Total Rewards Program, competitive pay, and benefits package.
If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements below. We’re interested in getting to know you and what you bring to the table!
Personalized benefits that support personal well-being and growth:
JLL recognizes the impact that the workplace can have on your wellness, so we offer a supportive culture and comprehensive benefits package that prioritizes mental, physical and emotional health.
About JLL –
We’re JLL—a leading professional services and investment management firm specializing in real estate. We have operations in over 80 countries and a workforce of over 102,000 individuals around the world who help real estate owners, occupiers and investors achieve their business ambitions. As a global Fortune 500 company, we also have an inherent responsibility to drive sustainability and corporate social responsibility. That’s why we’re committed to our purpose to shape the future of real estate for a better world. We’re using the most advanced technology to create rewarding opportunities, amazing spaces and sustainable real estate solutions for our clients, our people, and our communities.
Our core values of teamwork, ethics and excellence are also fundamental to everything we do and we’re honored to be recognized with awards for our success by organizations both globally and locally.
Creating a diverse and inclusive culture where we all feel welcomed, valued and empowered to achieve our full potential is important to who we are today and where we’re headed in the future. And we know that unique backgrounds, experiences and perspectives help us think bigger, spark innovation and succeed together.
