Senior Data Engineer

Golden Technology; United States; On-site; USD 80,000 - 120,000; Full time

Job summary

Golden Technology is seeking candidates for a role focused on Supply Chain Data Strategy and Cloud Operations. Responsibilities include developing solutions to meet business outcomes, analyzing and designing data architectures, and promoting data reuse. Successful candidates will leverage tools like Databricks and have strong skills in SQL, Python, and PySpark, contributing to the enterprise's strategic data initiatives.

Qualifications

• Core knowledge in data strategy and cloud operations.
• Ability to analyze, design, and develop enterprise data architectures.
• Familiar with enterprise standards for data domains.

Responsibilities

• Develop and deliver technological responses to business outcomes.
• Design and develop information architecture deliverables.
• Promote the reuse of data assets and manage the data catalog.

Skills

SQL

Python

PySpark

Data Architecture

Data Analysis

Tools

Databricks

Job description

Overview

Supply Chain Data Strategy and Cloud Operation are core to the and it operations.

With sales of over $100 B, the capability of delivering critical data and building cloud first solutions our teams offer are one of the most critical capabilities of the company.

We’re looking for individuals who can bring their core set of knowledge as well as learn new tools to provide new data and capabilities to support ever growing Supply Chain.

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.
• 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