Job Responsibilities :

- Lead end-to-end data engineering and data modeling initiatives, including requirements gathering, solution design, development, testing, deployment, and operational support.

- Develop scalable, high-performance data pipelines using PySpark and Apache Spark for data ingestion, transformation, integration, and loading across enterprise 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, and reliability.

- 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 delivery teams.

- Strong expertise in Databricks/Snowflake (or similar ETL tools), PySpark, Apache Spark, Spark SQL, and Python for designing, developing, and optimizing high-volume data pipelines and distributed data processing solutions.

- Proven experience in designing Conceptual, Logical, and Physical Data Models, including ER Modeling, Dimensional Modeling, Data Vault, and normalization/denormalization techniques.

- 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 best practices.