Data Architect Lead

20 hours ago

Islamabad, Islamabad, Pakistan beBee Careers Full time

About the Role

This position requires designing, building, and maintaining high-performance, large-scale complex data architectures using Python, SQL, PySpark, Azure, Databricks, Synapse, Azure Data Lake, DevOps, and cloud-based large-scale data applications.

Key responsibilities include architecting, designing, developing, testing, and maintaining big data products, constructing and fine-tuning Apache Spark jobs and clusters within the Databricks platform, migrating out of Azure Synapse to Azure Data Lake or other technologies, and evaluating and implementing new technologies and tools to improve data integration, data processing, storage, and analysis.

The ideal candidate will have a strong background in data engineering with expertise in Databricks, Azure, DevOps, and experience with cloud-based large-scale data applications. They will be skilled in programming languages such as Python, Scala, and proficient in writing efficient and optimized code for data integration, migration, storage, processing, and manipulation.

A strong understanding of big data principles, techniques, and best practices is required, along with experience with scalable and distributed Data Processing Technologies such as Spark/PySpark, DBT, and Kafka. The candidate will also be skilled in designing and implementing efficient ELT/ETL processes in Azure and Databricks and using open-source solutions being able to develop custom integration solutions as needed.

Responsibilities

  • Architect, design, develop, test, and maintain high-performance, large-scale, complex data architectures.
  • Contribute to detailed design, architectural discussions, and customer requirements sessions.
  • Actively participate in the design, development, and testing of big data products.
  • Construct and fine-tune Apache Spark jobs and clusters within the Databricks platform.
  • Migrate out of Azure Synapse to Azure Data Lake or other technologies.
  • Assess best practices and design schemas that match business needs for delivering a modern analytics solution.
  • Design and implement data models and schemas that support efficient data processing and analytics.
  • Collaborate with cross-functional teams to understand data requirements and develop data solutions.

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