Data Architect - Clark, United States - GEP

GEP Clark, United States

3 weeks ago

Description

Company Overview

GEP is a diverse, creative team of people passionate about procurement. We invest ourselves entirely in our client's success, creating strong collaborative relationships that deliver extraordinary value year after year. Our clients include market global leaders with far-flung international operations, Fortune 500 and Global 2000 enterprises, leading government and public institutions.

We deliver practical, effective services and software that enable procurement leaders to maximise their impact on business operations, strategy and financial performance. That's just some of the things that we do in our quest to build a beautiful company, enjoy the journey and make a difference. GEP is a place where individuality is prized, and talent respected. We're focused on what is real and effective. GEP is where good ideas and great people are recognized, results matter, and ability and hard work drive achievements. We're a learning organization, actively looking for people to help shape, grow and continually improve us.

Are you one of us?

GEP is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, ethnicity, color, national origin, religion, sex, disability status, or any other characteristics protected by law. We are committed to hiring and valuing a global diverse work team.

For more information please visit us on or check us out on

What you will do

We are seeking a highly skilled and results-oriented Data Architect with a demonstrated track record of driving innovation and leading high-performance teams. The ideal candidate will have extensive experience in enterprise data warehousing, business intelligence, and cloud-based data solutions. With a focus on advanced analytics, machine learning, and data-driven insights, the Data Architect will play a pivotal role in propelling revenue growth and implementing efficient data solutions and automation strategies.

Key Responsibilities:

Lead the design, development, and implementation of enterprise data architecture solutions, including data warehouses, data lakes, and analytics platforms.
Collaborate with cross-functional teams to understand business requirements and translate them into scalable and sustainable data architecture solutions.
Drive the adoption of cloud-based data technologies, leveraging platforms such as Databricks Lakehouse, Azure Synapse, and Power BI.
Manage the analytics needs for all company software products, ensuring data availability, quality, and security.
Build and lead global teams of engineers across various data projects, fostering a culture of innovation and excellence.
Develop and maintain data governance policies and procedures to ensure data integrity and compliance with regulatory standards.
Stay current with emerging technologies and industry trends in data architecture, analytics, and machine learning.
Provide technical leadership and mentorship to junior team members, promoting professional growth and development.

What you should bring

Bachelor's or Master's degree in Computer Science, Information Technology, or related field.
Proven experience in leading data architecture projects from conception to implementation.
Extensive knowledge of cloud-based data technologies, including Databricks Lakehouse, Azure Synapse, and Power BI.
Strong understanding of data governance principles and regulatory compliance standards.
Excellent leadership and communication skills, with the ability to collaborate effectively with cross-functional teams.
Demonstrated ability to mentor and develop junior team members.
Continuous learner with a passion for staying updated with emerging technologies and industry trends.

Preferred Qualifications:

Experience working in a global organization with distributed teams.
Certifications in relevant technologies such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, or similar.
Familiarity with agile development methodologies.
Experience in implementing machine learning solutions within a data architecture context.

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