Databricks Developer

Fa Ewjt Saasfaprod1; Gurugram District; On-site; INR 900,000 - 1,800,000; Full time

Job summary

Fa Ewjt Saasfaprod1 is seeking a Databricks Developer to design, build, and optimize scalable data pipelines and lakehouse solutions using Databricks. The role is hands-on and requires strong data engineering expertise.

Location is Gurgaon/Bangalore with 5 days in office. Full-time role with 5+ years experience; responsibilities include ETL/ELT on Databricks, Delta Lake architectures, CI/CD, and ensuring data quality.

Qualifications

• 3+ years in data engineering with Databricks hands-on experience.
• Strong SQL knowledge and experience with large-scale datasets.
• Proficient in PySpark and Spark SQL.
• Delta Lake and lakehouse patterns knowledge.
• Familiarity with CI/CD and Git for data pipelines.
• Understanding of data quality and observability.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines on Databricks.
• Develop data ingestion frameworks and end-to-end batch/streaming workflows.
• Implement Delta Lake-based architectures with versioning, schema evolution, and ACID compliance.
• Collaborate with stakeholders to translate data needs into scalable solutions.
• Manage Databricks clusters, jobs, and notebooks for performance and cost efficiency.
• Ensure data quality and observability through monitoring and validation.

Skills

Databricks

PySpark

Spark SQL

Delta Lake

SQL

CI/CD

Git

Data quality

Tools

Airflow

Job description

Role: Databricks Developer

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.

Location:Gurgaon/Bangalore (5 days mandatory work from office)

Type:Full-time

Experience:5+ years

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.

Must have Skills & Experience

• 3+ years of experience in data engineering with 2+ 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.

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.

3+ years of experience in data engineering with 2+ years of hands on expertise in Databricks.