Senior Databricks Developer

VSquare Systems Pvt. Ltd.; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

VSquare Systems Pvt. Ltd. seeks a Data Engineering + Data Modelling Lead to drive end-to-end data initiatives. You will architect scalable pipelines with PySpark/Spark, build enterprise data models, and lead ETL/ELT development across platforms, including Databricks and Snowflake.

The role requires guiding teams, defining data governance and quality frameworks, and optimizing workloads for performance. Bengaluru-based, with strong cloud and data warehousing experience.

Qualifications

• 6-8 years of experience in Data Engineering, ETL/ELT Development and Data Modeling.
• Experience leading enterprise-scale data initiatives and delivery teams.

Responsibilities

• Lead end-to-end data engineering and data modeling initiatives across the enterprise.
• Develop scalable data pipelines using PySpark and Spark for ingestion, transformation, and loading across platforms.
• Define enterprise-wide data models (conceptual, logical, physical) to support business and analytics.
• Design and maintain database schemas, tables, views, and data structures for transactional and analytical workloads.
• Lead ETL/ELT framework development and reusable components for efficient processing.
• Implement data quality, profiling, validation, and monitoring to ensure data accuracy and reliability.
• Optimize data models, pipelines, and Spark workloads for scalability and performance.
• Document data lineage, ETL workflows, metadata, and technical designs clearly.
• Provide technical leadership and mentorship to development teams.

Skills

PySpark

Apache Spark

Python

Data Modeling

ETL/ELT Development

Databricks

Snowflake

SQL

Airflow

Kafka

Tools

Databricks

Snowflake

AWS

Azure

Airflow

Kafka

NiFi

Oracle

SQL Server

Job description

Job Title: Data Engineering + Data Modelling Lead

Experience: 3-8 Years

Job Responsibilities:

• Lead end-to-end data engineering and data modeling initiatives, including requirements gathering, solution design, development, testing, deployment, andoperational support.
• Develop scalable, high-performance data pipelines using PySpark and Apache Spark for data ingestion, transformation, integration, and loading acrossenterprise platforms.
• Define and implement enterprise-wide conceptual, logical, and physical data models to support business, analytical, and operational requirements.
• Design and maintain database schemas, tables, views, indexes, and data structures that support both transactional and analytical workloads.
• Lead the development of ETL/ELT frameworks and reusable components to ensure efficient and standardized data processing.
• Implement data quality frameworks, validation rules, profiling techniques, and monitoring processes to ensure data accuracy, completeness, consistency, andreliability.
• Optimize data models, ETL pipelines, database performance, and Spark workloads to improve scalability, processing efficiency, and query performance.
• Document data models, data lineage, ETL workflows, metadata, and technical designs in a clear and comprehensive manner.
• Provide technical leadership and mentorship to development teams.

Required Experience & Skills:

• 6-8 years of experience in Data Engineering, ETL/ELT Development, and Data Modeling, with demonstrated experience leading enterprise-scale data initiatives and deliveryteams.
• Strong expertise in Databricks/Snowflake(or similar ETL tools), PySpark, Apache Spark, Spark SQL, and Python for designing, developing, and optimizing high-volume datapipelines and distributed data processing solutions.
• Proven experience in designing Conceptual, Logical, and Physical Data Models, including ER Modeling, Dimensional Modeling, Data Vault, and normalization/denormalizationtechniques.
• Hands-on experience with data lake, data warehouse, and cloud-based data platforms such as Databricks, Snowflake, AWS, or Azure.
• Strong knowledge of relational databases and performance tuning, including Oracle, SQL Server, PostgreSQL, Snowflake, schema design, indexing, and query optimization.
• Experience with data integration and orchestration technologies such as Apache Airflow, Kafka, NiFi, or equivalent enterprise data movement tools.
• Solid understanding of data governance, metadata management, data lineage, and data quality frameworks, with the ability to establish enterprise data standards and bestpractices.