Data Architect

7 days ago

New York, New York, United States DRUM UPSKILL Full time
About the Role

DRUM UPSKILL is seeking a highly skilled Data Engineer to join our dynamic team and contribute to our mission of sustainability and innovation. As a Data Engineer, you will play a critical role in designing, architecting, and implementing a comprehensive data lake solution that integrates various data sources.

Key Responsibilities
  • Data Lake Design and Implementation: Design and implement a scalable and secure data lake solution that integrates multiple data sources, including REST APIs, AWS S3 buckets, GCP BigQuery, and other on-prem/cloud databases.
  • Directed Acyclic Graphs (DAGs) Development: Develop and maintain DAGs using Apache Airflow for efficient data workflows, ensuring data quality, integrity, and security.
  • Data Extraction, Transformation, and Loading (ETL): Extract, transform, and load data from various sources, including structured and unstructured datasets, and manage data streams for both clinical measurements and instrument data.
  • Database Management: Manage and optimize Snowflake databases to ensure high performance and reliability, and collaborate with cross-functional teams to understand data requirements and deliver solutions that meet business needs.
  • Containerization and CI/CD: Implement containerization and use container tools for efficient deployment and scaling of data solutions, and utilize GitHub and GitHub Actions for version control and CI/CD pipelines.
Requirements
  • Proven Experience: Proven experience in designing and implementing data lake solutions in cloud environments (GCP/AWS).
  • Strong Proficiency: Strong proficiency in Snowflake database management, Apache Airflow, and DAG development.
  • ETL Experience: Ability to extract, transform, and load data from diverse sources, including REST APIs, AWS S3, GCP BigQuery, and other databases.
  • Database Management: Experience working with both structured and unstructured datasets, and expertise in database management, ETL processes, and data architecture.
  • Containerization: Proficiency with GitHub and GitHub Actions for version control and CI/CD, and experience with containerization and container tools (e.g., Docker, Kubernetes).
Preferred Skills
  • Clinical Data Background: Clinical data background with experience in managing and analyzing clinical datasets.
  • Veeva Vault Integration: Experience with Veeva Vault integration for managing clinical data and documents.
  • Machine Learning: Experience with implementing or enhancing data sets for machine learning applications.
  • Professional Certification: Professional Data Engineer certification or equivalent experience.

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