Senior Data Engineer

Golden Technology is a leading company in the supply chain industry with sales exceeding $100 billion. They are seeking a Senior Data Engineer to develop and deliver technological solutions that enhance data capabilities and support their supply chain operations.

Responsibilities:

• Accountable for developing and delivering technological responses to targeted business outcomes
• Analyze, design and develop enterprise data and information architecture deliverables, focusing on data as an asset for Supply Chain and the overall enterprise
• Understand and follow reusable standards, design patterns, guidelines, and configurations to deliver valuable data and information across the enterprise, including direct collaboration with where needed
• Create and leverage Databricks notebooks to source, shape and store data using SQL, Python, PySpark
• Utilize enterprise standards for data domains and data solutions, focusing on simplified integration and streamlined operational and analytical uses
• Ensure there is clarity between ongoing projects, escalating when necessary, including direct collaboration
• Define high-level migration plans to address the gaps between the current and future state
• Analyze technology environments to detect critical deficiencies and recommend solutions for improvement
• Promote the reuse of data assets, including the management of the data catalog for reference

Requirements:

• Accountable for developing and delivering technological responses to targeted business outcomes
• Analyze, design and develop enterprise data and information architecture deliverables, focusing on data as an asset for Supply Chain and the overall enterprise
• Understand and follow reusable standards, design patterns, guidelines, and configurations to deliver valuable data and information across the enterprise, including direct collaboration with where needed
• Demonstrate the company's core values of respect, honesty, integrity, diversity, inclusion and safety
• Create and leverage Databricks notebooks to source, shape and store data using SQL, Python, PySpark
• Utilize enterprise standards for data domains and data solutions, focusing on simplified integration and streamlined operational and analytical uses
• Ensure there is clarity between ongoing projects, escalating when necessary, including direct collaboration
• Define high-level migration plans to address the gaps between the current and future state
• Analyze technology environments to detect critical deficiencies and recommend solutions for improvement
• Promote the reuse of data assets, including the management of the data catalog for reference