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Tier-1 brand, mid-level generalist data role, metro location, and broad required tech stack increase competition.
Core data engineering skills (Python, Spark, Kafka, cloud) are readily transferable across industries.
Explicit 3+ years plus mandatory cloud, streaming, and specific tech stack requirements indicate strict filtering.
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Develop and operate modern data architectures, including data pipelines and management solutions leveraging relational and non-relational databases to support enterprise data strategy.
Partner globally to design and build scalable data solutions that ingest, cleanse, normalize, and structure diverse datasets across multiple internal and external sources.
Contribute to data infrastructure and processes to advance JLL towards a more sophisticated, agile, and robust target state data environment across business applications like CRM, finance, HR, and sales tools.
Bachelor’s degree in Information Science, Computer Science, Mathematics, Statistics, or a related quantitative discipline.
3+ years of overall work experience in data engineering or related roles.
Hands-on experience with Python, Kafka, Spark Streaming, Azure SQL Server, Cosmos DB/Mongo DB, Azure Event Hubs, Azure Data Lake Storage, and Azure Search.
On-site work location in Bengaluru, Karnataka, India.
Experienced with cloud-based data pipeline construction and event/queue-driven data streaming in a data lake environment.
Familiar with AI tools such as Cursor, Databricks Assistant, and Claude, with AI agent development as an advantage.
Ability to work effectively as an individual contributor in a fast-paced, diverse, and cross-functional global team environment.