JLL supports the Whole You, personally and professionally.
Key ResponsibilitiesData Engineering & Pipeline DevelopmentDesign, build, and maintain scalable data ingestion, transformation, and serving pipelines using Python and PySpark on Databricks
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 productive 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
📌 Data Engineer 2 (Bengaluru)
🏢 JLL
📍 Bengaluru