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:New York City, NY; Kansas City, Missouri; St. Paul, Minnesota; Providence , RI and Newark, DE.

 

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.

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 Python for data manipulation and analytics.

Experience with data modeling and architecture for both analytics and transactional systems.

Cloud and Big Data Experience:

Competence with at least one major cloud data platform (Azure, AWS, Google Cloud), demonstrating practical application in data engineering projects.

Exposure to Big Data technologies such as Spark and Cloud ETL tools like Databricks, with an understanding of their scalability and real-time processing capabilities.

Methodology and Tools:

Experience with Agile and SCRUM methodologies, contributing to project delivery in a dynamic development environment.

Practical knowledge in developing data models for integration and analysis supporting business intelligence and data analytics initiatives.

Additional Qualifications:

Any combination of education, training, and experience that provides the required knowledge, skills, and abilities to perform the essential functions of the job

 

Preferred Qualifications:

Experience with the Microsoft Azure technology stack or similar technologies in competing platforms.

Practical knowledge of data analytics and visualization tools to aid in data-driven decision making and reporting.

 

Certifications & Licenses:

The following certifications, while not mandatory are are highly recommended:

Professional certification in Agile and SCRUM methodologies (e.g., Certified ScrumMaster (CSM), SAFe Agilist).

 

Certifications in Python and SQL programming (e.g., Microsoft Certified: Python Programming Specialist, Oracle SQL Certification).

 

Certifications in cloud services relevant to the job (e.g., AWS Certified Solutions Architect, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate).

 

Big Data certifications (e.g., Cloudera Certified Professional (CCP): Data Engineer, Databricks Certified Professional Data Scientist).

 

Willing to attain certification, if not currently certified.

 

Required Knowledge, Skills & Abilities:

Knowledge

Fluent Communication: Ability to clearly communicate complex data concepts and updates to both technical and non-technical stakeholders.

Data Analysis: Proficiency in analyzing datasets to derive insights and inform business strategies.

SQL and Python: Strong skills in SQL and Python for data analysis, manipulation, and scripting.

ETL/ELT Architecture: Good understanding of developing and managing ETL and ELT architectures using various tools and frameworks.

Cloud Experience: Practical experience with cloud platforms such as Azure, AWS, or Google Cloud, and familiarity with their data services and tools.

Big Data Concepts: Basic knowledge of Big Data technologies and frameworks, including Spark and Cloud ETL tools like Databricks.

Agile and SCRUM Knowledge: Understanding of Agile methodologies and SCRUM practices, with the ability to apply them in project management and daily operations.

Data Quality and Governance: Awareness of data quality standards and governance practices to maintain data integrity and compliance.

Skills

Collaboration: Ability to collaborate effectively with cross-functional teams, including data scientists, BI analysts, and software engineers.

Mentorship: Capability to assist in mentoring junior engineers and contribute to the professional growth of the team.

Innovation and Continuous Learning: Dedication to staying updated on industry trends and new technologies in data engineering, and applying this knowledge to enhance job performance.

Abilities

Customer Interaction: Comfortable interacting with customers in one-on-one settings or in large groups.

Independent Work: Ability to work independently with minimal supervision, especially in remote settings.

Feedback Integration: Skill in incorporating feedback into the development process, actively seeking consistent and constructive critiques.

Accountability: Strong sense of responsibility and ownership in completing tasks and achieving results.

For applicants who will perform this position in New York City or Westchester County, the proposed annual salary is $100,000 to $115,000 a year.  For applicants who will perform this position outside of New York City or Westchester County, salary will reflect local market rates and be commensurate with the applicant’s skills, job-related knowledge, and experience.

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