Lead Assistant Manager

Role Overview
The Databricks Data Engineer will be responsible for designing, building, and optimizing scalable data pipelines and lakehouse solutions using Databricks. The role requires strong hands‑on experience in data engineering, distributed data processing.

Mandatory 5 days work from office in Gurgaon.

Key Responsibilities

• Design, build, and maintain ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake.
• Develop and optimize data ingestion frameworks, data transformations, and end‑to‑end workflows for batch and streaming use cases.
• Implement Delta Lake‑based architectures, including versioning, schema evolution, and ACID‑compliant pipelines.
• Work with stakeholders to understand data requirements and translate them into scalable data engineering solutions.
• Manage and optimize Databricks clusters, jobs, and notebooks for performance and cost efficiency.
• Ensure data quality, reliability, and observability through validation frameworks and monitoring.
• Contribute to data modeling, metadata management, and best practices within the data platform.
• Collaborate closely with data scientists, analysts, and business teams to support analytics and ML workloads.

Must have Skills & Experience

• 5+ years of experience in data engineering with 3+ years of hands on expertise in Databricks.
• Hands‑on experience with Spark (PySpark/Spark SQL) and distributed data processing.
• Solid SQL knowledge and experience working with large-scale datasets
• Strong understanding of Delta Lake, medallion architecture, and scalable lakehouse patterns.
• Good understanding of CI/CD, Git, and modern DevOps practices for data pipelines.
• Familiarity with structured/unstructured data, data quality frameworks, and performance tuning.

Graduate in Computer Science, Data Science, or related field. 6-9 years of experience in data engineering or a related field.

• Design, build, and maintain ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake.
• Develop and optimize data ingestion frameworks, data transformations, and end‑to‑end workflows for batch and streaming use cases.
• Implement Delta Lake‑based architectures, including versioning, schema evolution, and ACID‑compliant pipelines.
• Work with stakeholders to understand data requirements and translate them into scalable data engineering solutions.
• Manage and optimize Databricks clusters, jobs, and notebooks for performance and cost efficiency.
• Ensure data quality, reliability, and observability through validation frameworks and monitoring.
• Contribute to data modeling, metadata management, and best practices within the data platform.
• Collaborate closely with data scientists, analysts, and business teams to support analytics and ML workloads.