Data Solutions Architect

1 week ago


Thiruvananthapuram, Kerala, India beBeeDatabricks Full time ₹ 15,00,000 - ₹ 20,00,000

Role Summary

We are seeking a highly skilled Databricks PySpark Developer to join our team in designing, developing and maintaining ETL processes. In this role, you will be instrumental in extracting, transforming and loading data from various sources into data lake and data warehouse.

Key Responsibilities
  • ETL Development:
    • Design and develop ETL processes using Databricks PySpark to extract, transform, and load data from heterogeneous sources into our data lake and data warehouse.
    • Optimize ETL workflows for performance and scalability, leveraging Databricks PySpark and Spark SQL to efficiently process large data volumes.
    • Implement robust error handling and monitoring mechanisms to proactively detect and resolve issues within ETL processes.
    • Design and implement data solutions following the Medallion Architecture principles, organizing data into Bronze, Silver, and Gold layers.
    • Ensure data is appropriately cleansed, enriched, and optimized at each stage to support robust analytics and reporting.
  • Data Pipeline Management:
    • Develop and maintain data pipelines using Databricks PySpark, ensuring data quality, integrity, and reliability throughout the ETL lifecycle.
    • Collaborate with data engineering, data science, and business intelligence teams to translate data requirements into efficient ETL workflows and pipelines.
  • Data Analysis and Query Optimization:
    • Write and optimize complex SQL queries for data manipulation, aggregation, and analysis within Databricks PySpark applications.
  • Project Coordination and Continuous Improvement:
    • Participate in project planning and coordination activities to ensure timely delivery of ETL solutions.
    • Stay updated on the latest developments in Databricks PySpark, Spark SQL, and related technologies, recommending and implementing best practices and optimizations.
    • Document ETL processes, data lineage, and metadata to facilitate knowledge sharing and ensure compliance with data governance standards.


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