A bachelor's degree in computer science, Data Science, Information Technology, or a related quantitative field, or equivalent education, training, and experience.
Six or more years of progressive data engineering experience with demonstrated ownership of complex, production-grade data platforms.
Expert-level SQL, including query optimization, indexing strategy, and execution plans, and Python, including PySpark, pipeline frameworks, and testing.
Deep hands-on experience with Azure data services, including Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Data Lake Storage.
Experience designing dimensional data models and data lake architecture at enterprise scale.
Experience building data pipelines that support machine learning feature engineering and model serving.
Strong background in data quality engineering, including automated validation, SLA enforcement, and lineage tracking.
Experience with relational databases such as SQL Server and Oracle, and migration from legacy to cloud-native platforms.
Knowledge of Azure data platform architecture and integration patterns, data lake and medallion architecture, data mesh concepts, and consumption layer design.
Knowledge of machine learning pipeline requirements, including feature engineering, training data preparation, and model data dependencies.
Knowledge of data governance frameworks, including metadata management, lineage, cataloging, access control, and regulatory compliance such as HIPAA.
Knowledge of Agile engineering practices, including sprint delivery, DevOps hygiene, CI/CD for data pipelines, and technical documentation standards.
Ability to communicate complex data concepts clearly to technical and non-technical stakeholders, including culturally sensitive communication with diverse individuals and groups.
Ability to work independently, manage competing priorities, take ownership of deliverables, incorporate feedback, and pursue continuous improvement.
Responsibilities
Own the design and delivery of complex data engineering solutions supporting enterprise analytics, AI, and reporting capabilities.
Drive technical decisions, set engineering standards, and ensure the reliability and scalability of the data platform across integrated enterprise source systems.
Architect, build, and own complex data pipelines for high-volume, high-criticality workstreams across the enterprise data platform.
Lead the design and implementation of ELT/ETL frameworks using SQL, Python, Azure Data Factory, Databricks, and Azure Synapse Analytics.
Establish pipeline reliability standards for monitoring, alerting, error handling, and recovery, and ensure the team adheres to them.
Drive the design of scalable data models for dimensional warehousing and data lake architectures on Azure.
Contribute to architectural decisions about data storage, partitioning, compute optimization, and consumption-layer design.
Lead migrations from legacy data solutions to modern cloud-native platforms while managing risk and business continuity.
Design and deliver feature pipelines and data preparation frameworks supporting machine learning model development and deployment.
Partner with Data Scientists to translate model requirements into production-grade data assets and feature stores.
Collaborate with Analytics Engineers to optimize data models for analytical consumption and reporting performance.
Define and implement data quality frameworks, including validation rules, SLAs, anomaly detection, and automated testing for pipeline outputs.
Lead data governance initiatives, including metadata management, lineage tracking, data cataloging with Microsoft Purview, and access control.
Ensure platform compliance with HIPAA, NYBCe data policies, and applicable regulatory requirements.
Mentor Data Engineers through code reviews, technical guidance, and architectural feedback.
Contribute to engineering standards, reusable frameworks, and technical documentation.
Participate in Agile ceremonies and uphold engineering discipline, including DevOps hygiene, sprint commitment, and delivery accountability.
Translate product and analytical requirements into sound engineering designs and delivery plans.
Desired Qualifications
Microsoft Certified: Azure Data Engineer Associate certification.
Databricks Certified Associate Developer for Apache Spark certification.
Responsibilities
As a Senior Data Engineer on DAPI's Tetris team, you will own the design and delivery of complex data engineering solutions that power NYBCe's enterprise analytics, AI, and reporting capabilities. Reporting to the Lead Data Engineer, you will drive technical decisions, set engineering standards, and ensure the reliability and scalability of DAPI's data platform across 49+ integrated enterprise source systems.
This role demands deep technical mastery in SQL, Python, and Azure cloud data engineering, combined with a product orientation—understanding how the data assets you build translate into decisions, reports, and AI outputs for the business. You will mentor Data Engineers, contribute to architectural direction, and serve as a technical anchor for delivery across the Tetris team's sprint cycles.
Advanced Pipeline Development & Ownership
Architect, build, and own complex data pipelines for high-volume, high criticality workstreams across NYBCe's enterprise data platform.
Lead the design and implementation of ELT/ETL frameworks using SQL, Python, Azure Data Factory, Databricks, and Azure Synapse Analytics.
Establish pipeline reliability standards—monitoring, alerting, error handling, and recovery protocols—and ensure adherence across the team.
Data Architecture & Platform Evolution
Drive the design of scalable data models supporting dimensional warehousing, data lake architectures on Azure.
Contribute to architectural decisions on data storage, partitioning, compute optimization, and consumption layer design.
Lead migrations from legacy data solutions to modern cloud-native platforms, managing risk and business continuity throughout.
AI & Analytics Enablement
Design and deliver feature pipelines and data preparation frameworks that support machine learning model development and deployment.
Partner with Data Scientists to translate model requirements into production-grade data assets and feature stores.
Collaborate with Analytics Engineers to ensure data models are optimized for analytical consumption and reporting performance.
Data Quality & Governance Leadership
Define and implement data quality frameworks—validation rules, SLAs, anomaly detection, and automated testing for pipeline outputs.
Lead data governance initiatives including metadata management, lineage tracking, data cataloging (Microsoft Purview), and access control.
Ensure platform compliance with HIPAA, NYBCe data policies, and applicable regulatory requirements.
Mentorship & Technical Leadership
Mentor Data Engineers—providing code reviews, technical guidance, and architectural feedback that elevates team capability.
Contribute to DAPI's engineering standards, reusable frameworks, and technical documentation.
Participate in Agile ceremonies and model strong engineering discipline—clear DevOps hygiene, sprint commitment, and delivery accountability.
Qualifications
Education:
Bachelor’s degree in computer science, Data Science, Information Technology, or a related quantitative field.
Experience:
6+ years of progressive experience in data engineering with demonstrated ownership of complex, production-grade data platforms.
Deep hands-on experience with Azure data services: Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage.
Proven experience designing dimensional data models and data lake architecture at enterprise scale.
Experience building data pipelines that directly support machine learning feature engineering and model serving.
Strong background in data quality engineering—automated validation, SLA enforcement, and lineage tracking.
Experience with relational databases (SQL Server, Oracle) and migration from legacy to cloud-native platforms.
Certifications & Licenses:
No certifications are required. The following are considered favorable:
Microsoft Certified: Azure Data Engineer Associate
Databricks Certified Associate Developer for Apache Spark
Knowledge:
Advanced SQL and Python for enterprise-scale data engineering—optimization, testing, and framework design.
Azure data platform architecture in depth—ADF, Databricks, Synapse, ADLS, and their integration patterns.
Modern data platform paradigms—data lake, medallion architecture, data mesh concepts, and consumption layer design.
Machine learning pipeline requirements—feature engineering, training data preparation, and model data dependencies.
Data governance frameworks—metadata management, lineage, cataloging, access control, and regulatory compliance (HIPAA).
Agile engineering practices—sprint delivery, DevOps hygiene, CI/CD for data pipelines, and technical documentation standards.
Skills:
Cultural competency and the ability to communicate effectively in a culturally sensitive manner with both individuals and groups from diverse backgrounds.
Architect and deliver complex, production-grade data pipelines that meet enterprise reliability and performance standards.
Design scalable data models and platform structures that serve analytics, reporting, and AI consumption patterns simultaneously.
Lead data quality engineering—automated testing, validation frameworks, SLA monitoring, and incident response.
Mentor and elevate Data Engineers through code review, architectural feedback, and knowledge transfer.
Translate product and analytical requirements into sound engineering designs and delivery plans.
Abilities:
Communicate complex data concepts clearly to both technical and non-technical stakeholders.
Work independently and manage competing priorities in a lean, fast-paced team environment.
Embrace accountability and take ownership of deliverables end-to-end.
Incorporate feedback constructively and seek continuous improvement.
Any combination of education, training, and experience equivalent to the requirements above that has supplied the necessary knowledge, skills, and experience to perform the essential functions of the job.
For applicants who will perform this position in Westchester County, the proposed annual salary is $135,000.00 to $150,000.00 per 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.
Unless otherwise specified, all posted opportunities are located in the New York or Greater Tri-State office locations.
Overview
Founded in 1964, New York Blood Center (NYBC) has served the tri-state area for more than 60 years, delivering 500,000 lifesaving blood products annually to 150+ hospitals, EMS and healthcare partners. NYBC is part of New York Blood Center Enterprises (NYBCe), which spans 17+ states and delivers one million blood products to 400+ U.S. hospitals annually. NYBCe additionally delivers cellular therapies, specialty pharmacy, and medical services to 200+ research, academic and biopharmaceutical organizations. NYBCe’s Lindsley F. Kimball Research Institute is a leader in hematology and transfusion medicine research, dedicated to the study, prevention, treatment and cure of bloodborne and blood-related diseases. NYBC serves as a vital community lifeline dedicated to helping patients and advancing global public health. To learn more, visit nybc.org. Connect with us on Facebook, X, Instagram, and LinkedIn.