Full Stack Data Engineer

Enterprise Data Platform Developer

We're seeking a highly skilled and experienced professional to play a pivotal role in the development and maintenance of our Enterprise Data Platform. In this role, you'll be responsible for designing, building, and optimizing scalable data pipelines within our Google Cloud Platform (GCP) environment. You'll work with GCP Native technologies like BigQuery, Dataflow, and Pub/Sub, ensuring data governance, security, and optimal performance. This is a fantastic opportunity to leverage your full-stack expertise, collaborate with talented teams, and establish best practices for data engineering.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Information Technology, Information Systems, Data Analytics, or a related field (or equivalent combination of education and experience).
• 5-7 years of experience in Data Engineering or Software Engineering, with at least 2 years of hands-on experience building and deploying cloud-based data platforms (GCP preferred).
• Strong proficiency in SQL, Java, and Python, with practical experience in designing and deploying cloud-based data pipelines using GCP services like BigQuery, Dataflow, and DataProc.
• Solid understanding of Service-Oriented Architecture (SOA) and microservices, and their application within a cloud data platform.
• Applicants must be able to work directly for Artech on W2.

Preferred Skills & Qualifications

• Experience with relational databases (e.g., PostgreSQL, MySQL), NoSQL databases, and columnar databases (e.g., BigQuery).
• Knowledge of data governance frameworks, data encryption, and data masking techniques in cloud environments.
• Familiarity with CI/CD pipelines, Infrastructure as Code (IaC) tools like Terraform and Tekton, and other automation frameworks.

Day-to-Day Responsibilities

• Design, build, and optimize scalable data pipelines within the GCP environment.
• Ensure data governance, security, and optimal performance using GCP Native technologies.
• Collaborate with talented teams to establish best practices for data engineering.