Senior 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 Senior Data Engineer, you'll be a pivotal figure in defining and advancing our data infrastructure vision. Reporting to the Director of Data Engineering, your role will be crucial in designing, implementing, and refining databases, data pipelines, and data interfaces to ensure scalability and performance. Your proficiency in SQL, Python, and cloud environments (Azure, AWS, or Google Cloud) will empower you to develop solutions that are both robust and optimally aligned with our strategic goals. With a solid grasp of Big Data concepts, including Spark and Cloud ETL tools like Databricks, you will enhance our capabilities in handling complex data challenges. By adopting Agile/SCRUM methodologies, you'll drive innovative and timely project deliveries. You'll also mentor junior engineers, promoting a culture of excellence and continuous improvement. As a senior member of our team, you will work closely with data scientists, BI teams, software engineers, and other stakeholders to translate complex data requirements into practical and impactful engineering strategies.

Candidates must be able to report into one of the following NYBCe locations:New York City, NY; Kansas City, Missouri; St. Paul, Minnesota; Providence , RI and Newark, DE.

 

Responsibilities:

• Data Pipeline Design & Optimization: Design, implement, and optimize robust and scalable data pipelines using SQL, Python, and cloud-based ETL tools such as Databricks. Ensure efficient data flow and processing to support large-scale data handling.
• Data Modeling: Develop and refine data models to accurately represent business processes, ensuring they're scalable and fully integrate with our extensive data architecture, including Big Data frameworks like Spark.
• Data Architecture: Enhance our overarching data architecture strategy, assisting in decisions related to data storage, consumption, integration, and management within cloud environments (Azure, AWS, or Google Cloud).
• Agile/SCRUM: Lead and contribute within Agile/SCRUM frameworks to ensure timely and efficient project deliveries. Actively participate in sprints and stand-ups, applying these methodologies to streamline development.
• Collaboration: Partner with data scientists, BI teams, and other engineering teams to understand and translate complex data requirements into actionable engineering solutions.
• Mentorship: Guide and mentor junior data engineers, promoting best practices in SQL, Python, and cloud technologies, and fostering a culture of continuous learning and improvement.
• Quality & Governance: Uphold and champion data quality standards and governance policies, ensuring reliability and compliance in all data-related tasks.
• Performance Tuning: Monitor and enhance the performance of data infrastructure, proactively identifying and resolving bottlenecks or inefficiencies in cloud and Big Data environments.
• Innovation: Stay abreast of emerging data engineering and AI technologies and methodologies, recommending and implementing innovative tools or practices as appropriate.
• Documentation: Generate comprehensive documentation for data processes, pipelines, and architectures to ensure clarity and ease of maintenance for the team, including detailed descriptions of cloud and Big Data implementations.

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:

• 7+ years of progressive experience in data engineering, with significant expertise in designing, implementing, and optimizing databases and data pipelines.
• Extensive hands-on experience with SQL Server, Oracle, or other relational database management systems (RDBMS).
• Proficiency in SQL and Python for advanced data manipulation and analytics.
• Demonstrated experience with data modeling and architecture for both analytics and transactional systems within large-scale environments.

Cloud and Big Data Experience: