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Mid-level popular Data Engineer in Bangalore with a strong employer brand and generalist requirements increases competition.
Core data engineering skills are broadly transferable across industries despite some agentic AI specialization.
Explicit 3–5 years plus mandatory Databricks/PySpark/Azure and AI integration skills raise shortlisting strictness.
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Design, build, and maintain scalable data ingestion, transformation, and serving pipelines using Python, PySpark, and orchestration tools on Databricks/Azure cloud.
Develop and integrate AI-augmented workflows and agentic AI capabilities (e.g., self-healing pipelines, anomaly detection, LLM-powered data querying) into enterprise-scale data engineering operations.
Build and maintain data models, Delta Lake tables, APIs (FastAPI), and infrastructure components ensuring data quality, pipeline reliability, and SLA compliance.
3–5 years of professional data engineering experience with strong Python, SQL, and PySpark skills.
Hands-on experience building and maintaining data pipelines on cloud platforms like Databricks or Azure Synapse.
Experience with data orchestration tools such as Azure Data Factory, Airflow, or Databricks Workflows.
Location requirement: On-site in Bengaluru, KA.
Experienced in AI/ML integration within data engineering, including use of LLM APIs and agent frameworks like LangGraph or LangChain.
Comfortable working with modern data architectures (Delta Lake, lakehouse) and cloud-native tools on Azure platform.
Capable of independently managing data domains and pipelines with focus on automation, observability, and data governance in an Agile environment.