Chief Data Infrastructure Specialist

1 day ago

Sahiwal, Punjab, Pakistan beBeeData Full time 800,000 - 1,000,000

About this position

We are seeking an experienced Senior Data Engineer to join our team and design, build, and maintain large-scale data architectures for integration, storage, processing, and analytics. The ideal candidate will have a strong background in Python, SQL, PySpark, Azure, Databricks, Synapse, Azure Data Lake, DevOps, and cloud-based applications.

This is a remote contract position that involves contributing to the architecture, design, and implementation of data products in the Aviation Industry. The successful candidate will develop in an Agile environment and be responsible for migrating from Synapse to Azure Data Lake or other technologies.

Key Responsibilities
  • Data Architecture Design: Architect, design, develop, test, and maintain high-performance, large-scale data architectures for data integration (batch and real-time), storage, processing, orchestration, and infrastructure; ensure scalability, reliability, and performance.
  • Design Discussions: Contribute to detailed design, architectural discussions, and customer requirements.
  • Big Data Product Development: Participate in design, development, and testing of big data products.
  • Apache Spark Optimization: Construct and optimize Apache Spark jobs and clusters within Databricks.
  • Migration and Deployment: Migrate from Azure Synapse to Azure Data Lake or other technologies.
  • Data Modeling: Design schemas and data models to support modern analytics (descriptive to prescriptive).
  • Code Development: Develop clear, maintainable code with automated testing.
  • Collaboration: Collaborate with cross-functional teams to understand data requirements and deliver reusable components.
  • Technology Evaluation: Evaluate and implement new technologies to improve data integration, processing, storage, and analysis.
  • Data Governance: Design, implement, and maintain data governance: cataloging, lineage, data quality.
  • Workload Optimization: Monitor and optimize workloads and clusters for performance.
  • Mentorship: Mentor junior team members and share best practices.
  • Documentation: Maintain documentation of solutions and configurations.
  • Best Practices: Promote best practices in data engineering, governance, and quality.

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