Azure Data Engineer - Spark, ETL & Data Warehouse Specialist

BULLIT RECRUITMENT SOLUTIONS LIMITED

Sydney

On-site

AUD 140,000 - 180,000

Full time

28 hours ago
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Job summary

BULLIT RECRUITMENT SOLUTIONS LIMITED in Sydney, Australia seeks a senior software data engineer with 8+ years in development and Agile environments to design and optimize large data pipelines. You will leverage Python and SQL, with Java experience a plus, to deliver scalable data platforms.

You will work with PySpark, Azure Synapse, ADF, and Azure Functions, building robust data models and ETL pipelines, while managing distributed batch processing systems and applying modern data warehousing

Qualifications

  • 8+ years in development/software engineering and experience in Agile organisations.
  • Strong Python and SQL skills; Java experience is a plus.
  • Hands-on in building complex data pipelines and distributed systems.
  • Experience with PySpark, Azure Synapse, ADF, Azure Functions, data models and ETL pipelines.

Responsibilities

  • Develop and optimize complex data pipelines.
  • Work with PySpark, Azure Synapse, ADF, Azure Functions and data models for ETL pipelines.
  • Set up and optimize SQL and NoSQL data stores (MS-SQL, ADLS Gen2).
  • Manage and evolve distributed systems and batch processing clusters.
  • Apply MLOps architecture and practices.
  • Implement modern data modeling and large-scale data warehousing.

Skills

Python
SQL
Agile
Java experience

Tools

Apache PySpark
Azure Synapse
Azure Data Factory (ADF)
Azure Functions
MS-SQL
ADLS Gen2

Job description

BULLIT RECRUITMENT SOLUTIONS LIMITED in Sydney, Australia seeks a senior software data engineer with 8+ years in development and Agile environments to design and optimize large data pipelines. You will leverage Python and SQL, with Java experience a plus, to deliver scalable data platforms.

You will work with PySpark, Azure Synapse, ADF, and Azure Functions, building robust data models and ETL pipelines, while managing distributed batch processing systems and applying modern data warehousing

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