Data Engineer

Overview

At New York Blood Center Enterprises (NYBCe), one of the most comprehensive blood centers in the world, our focus is on cultivating excellence by merging cutting-edge innovation with diligent customer service, groundbreaking research, and comprehensive program and service development. Join us as we work towards meeting and exceeding the growing needs of our diverse communities, further our lifesaving strategic goals in a rapidly changing environment, and expand our impact on the local, national, and global communities we serve.

Responsibilities

As a Data Engineer, you will play a crucial role in developing and optimizing our data infrastructure to support organizational growth and data-driven decision-making. Reporting to Senior/Lead Data Engineer, you will contribute to the design, implementation, and maintenance of scalable data pipelines and architectures within our cloud and on-premises environments. Leveraging your proficiency in SQL, Python, and your experience with cloud platforms (Azure, AWS, or Google Cloud), you will help enhance our data processing capabilities and integrate new data management technologies. Your understanding of ETL/ELT frameworks, combined with a practical knowledge of Big Data tools such as Spark and Databricks, will be instrumental in improving our data workflows. By working collaboratively with cross-functional teams and utilizing Agile methodologies, you will ensure that our data solutions are robust, timely, and aligned with business objectives. This role provides a dynamic opportunity to develop your technical skills and advance your career in data engineering, while making a significant impact on our operations.

 

Candidates must be able to report into one of the following NYBCe locations: Rye, New York; Kansas City, Missouri; St. Paul, Minnesota; Lincoln, Nebraska Providence , Rhode Island and Newark, Delaware.

 

Responsibilities:

• Data Pipeline Design & Optimization: Assist in the design and implementation of robust and scalable data pipelines using SQL, Python, and cloud-based ETL tools such as Data Bricks. Support the optimization of data flow and processing to meet business needs.
• Data Modeling: Collaborate in developing and refining data models to accurately represent business processes, ensuring scalability and integration with our data architecture, including frameworks like Spark.
• Data Architecture: Support the enhancement of our data architecture strategy, contributing to decisions related to data storage, consumption, integration, and management in cloud environments (Azure, AWS, or Google Cloud).
• Agile/SCRUM: Participate in Agile/SCRUM frameworks to support timely and efficient project deliveries. Engage in sprints and stand-ups, applying these methodologies to assist in development processes.
• Collaboration: Work closely with data scientists, BI teams, and other engineering teams to help understand and implement complex data requirements into engineering solutions.
• Learning & Development: Engage in continuous learning to enhance skills in SQL, Python, and cloud technologies, under the mentorship of more senior engineers.
• Quality & Governance: Contribute to the adherence and promotion of data quality standards and governance policies, ensuring reliability and compliance in data-related tasks.
• Performance Monitoring: Assist in monitoring the performance of data infrastructure, helping to identify and resolve inefficiencies or bottlenecks in cloud and Big Data environments.
• Innovation: Keep up-to-date with emerging data engineering technologies and methodologies, and participate in implementing new tools or practices as guided by senior team members.
• Documentation: Help create and maintain documentation for data processes, pipelines, and architectures to ensure clarity and ease of maintenance for the team.

Other Secondary Functions:

• Responsible for sharing on-call rotation and off-hours outage escalations support with colleagues.
• Provide support for existing legacy data solutions and develop migration paths to new platforms as required / necessary.

Qualifications

Required Minimum Education & Experience:

 

Education:

Bachelor’s Degree in Computer Science, Data Science, Information Technology, or other quantitative disciplines such as Science, Statistics, Economics, or Mathematics.

 

Essential Experience:

• 4+ years of experience in data engineering, focusing on the design, implementation, and maintenance of data and data pipelines.
• Hands-on experience with SQL Server, Oracle, or other relational database management systems (RDBMS).
• Proficiency in SQL and Pyt