Analytics Engineer

New York Blood Center Enterprises•Rye, NY

13d•$119,000 - $142,000•Hybrid

About The Position

The Analytics Engineer is a highly technical, business-facing role responsible for delivering deep analytical insights, rapid analytics solutions, and advanced data products in support of discovery and research teams. This role bridges analytics, data engineering, and applied data science, transforming complex and ambiguous business questions into high-quality analyses, dashboards, and data-driven recommendations. The Analytics Engineer operates with a high degree of independence, demonstrates strong ownership of outcomes, and is expected to rapidly acquire domain knowledge. The role emphasizes advanced SQL and Python proficiency, exploratory analysis, and an active interest in data science and AI-driven solutions.

Requirements

Bachelor’s degree in data science, Computer Science, Engineering, Mathematics, Business Analytics, or a related field.
5+ years of experience in advanced analytics, data analysis, analytics engineering, or similar roles.
Demonstrated experience delivering business-facing analytical solutions in complex environments.
Strong experience with SQL and Python applied to real-world analytics problems.
Advanced knowledge of SQL and data models.
Strong understanding of analytics workflows, exploratory analysis, and data validation.
Working knowledge of data science and AI concepts.
Understanding of data governance and quality principles.
Cultural competency and the ability to communicate effectively in a culturally sensitive manner with both individuals and groups from diverse backgrounds.
Exceptional analytical and critical thinking skills.
Ability to rapidly analyze, synthesize, and communicate insights.
Strong time management with a focus on delivery and accountability.
Clear written and verbal communication skills.
Ability to operate independently with minimal oversight.
Ability to manage ambiguity and drive work from problem definition to solution.
Ability to engage confidently with senior stakeholders and discovery teams.
Ability to continuously learn and adapt in a fast-evolving analytics environment.
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.

Responsibilities

Conduct deep, hypothesis-driven exploratory data analysis to support discovery, research, and operational decision-making.
Translate ambiguous business questions into structured analysis.
Serve as a partner to discovery team, providing analytics support.
Design, develop, and deliver high-quality dashboards, reports, and ad-hoc analyses with fast turnaround times.
Produce executive- and stakeholder ready outputs with minimal rework.
Balance speed and rigor while maintaining high standards of data quality.
Develop complex SQL queries and conduct performance and quality checks.
Use Python for data analysis, feature engineering, and analytical modeling.
Build reusable analytical assets to accelerate future discovery efforts.
Collaborate and support data science team in developing AI/ML uses cases.
Apply statistical experimentation modeling techniques where appropriate.
Maintain active curiosity and learning in analytics, data science, and AI.
Partner closely with business stakeholders, discovery team to align analytics solutions with business objectives.
Clearly communicate analytical findings, assumptions, and limitations to technical and non-technical audiences.
Contribute to Agile/SCRUM ceremonies and deliver work predictably within sprint commitments.
Continuously improve analytics processes, tooling, and delivery practices.
Support modernization and migration initiatives from legacy reporting platforms.
Participate in on-call or escalation support rotations as needed.
Mentor peers on analytics best practices when appropriate.