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 effective 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

📌 Data Engineer 2 (Bengaluru)
🏢 JLL
📍 Bengaluru