Job Description

Senior GCP Engineer with Vertex AI and MLOps

Remote (Cincinnati, OH)

Contract

Required:

• Senior GCP engineer with atleast 7 years on project experience .
• Worked with Vertex AI and ML/Ops .
• Can work independently with the data science team.
• Overall Communication needs to be good.

Description:

• As a Sr. Data Engineer, you will have the opportunity to lead the development of innovative data solutions, enabling the effective use of data across the organization.
• You will be responsible for designing, building, and maintaining robust data pipelines and platforms to meet business objectives, focusing on data as a strategic asset.
• A strong emphasis will be placed on expertise in GCP, Vertex AI, and advanced feature engineering techniques.

Key Responsibilities:

• Provide Technical Leadership
• Build and Maintain Data Pipelines: Design, build, and maintain scalable, efficient, and reliable data pipelines to support data ingestion, transformation, and integration across diverse sources and destinations, using tools such as Kafka, Databricks, and similar toolsets.
• Drive Digital Innovation: Leverage innovative technologies and approaches to modernize and extend core data assets, including SQL-based, NoSQL-based, cloud-based, and real-time streaming data platforms.
• Implement Feature Engineering: Develop and manage feature engineering pipelines for machine learning workflows, utilizing tools like Vertex AI, BigQuery ML, and custom Python libraries.
• Implement Automated Testing: Design and implement automated unit, integration, and performance testing frameworks to ensure data quality, reliability, and compliance with organizational standards.
• Optimize Data Workflows
• Mentor Team Members
• Draft and Review Documentation: Draft and review architectural diagrams, interface specifications, and other design documents to ensure clear communication of data solutions and technical requirements.
• Cost/Benefit Analysis: Present opportunities with cost/benefit analysis to leadership, guiding sound architectural decisions for scalable and efficient data solutions

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