Sr. Data Engineer - Remote

Title: Sr. Data Engineer
Location: Remote
Pay Rate: $70/hr - $75/hr

Job 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. Your role will involve collaboration with cross-functional teams, leveraging cutting-edge technologies, and ensuring scalable, efficient, and secure data engineering practices. A strong emphasis will be placed on expertise in Google Cloud Platform, Vertex AI, and advanced feature engineering techniques.

Key Responsibilities

• Provide Technical Leadership: Offer technical leadership to ensure clarity between ongoing projects and facilitate collaboration across teams to solve complex data engineering challenges.


• 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: Optimize data workflows for performance, cost efficiency, and scalability across large datasets and complex environments.


• Mentor Team Members: Mentor team members in data principles, patterns, processes, and practices to promote best practices and improve team capabilities.


• 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.