Databricks Developer

Job Title: Data Engineering + Data Modelling Lead
Experience: 3-8 Years
Location: Bangalore (Preferred) / Remote
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.

Work arrangement
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