Senior Associate, Analytics Data Engineer

New York Life•New York, NY

15h•Hybrid

About The Position

New York Life is seeking a Senior Associate, Analytics Data Engineer to join their technology-driven organization. This role involves designing, implementing, and automating robust data pipelines and models to support analytics and business intelligence needs. The engineer will leverage expert-level SQL and Python, along with cloud platforms like AWS and big data frameworks such as Redshift, to build scalable and secure data solutions. Collaboration with analysts, data scientists, and business stakeholders is key to translating requirements into actionable data solutions. The position also involves developing and enforcing development standards, conducting quality assurance, and providing troubleshooting support for data systems. Staying current with industry trends and improving the technology stack are also important aspects of the role.

Requirements

Master's degree in Computer Science, Computer Engineering, Business Analytics or related field (willing to accept foreign education equivalent) plus three (3) years of experience as a Data Analyst or Data Engineer supporting data science and analytics through data engineering, pipeline development, and cross-functional collaboration across data, IT, and business teams.
Alternatively, Bachelor's degree in Computer Science, Computer Engineering, Business Analytics or related field (willing to accept foreign education equivalent) plus five (5) years of experience as a Data Analyst or Data Engineer supporting data science and analytics through data engineering, pipeline development, and cross-functional collaboration across data, IT, and business teams.
Utilizing advanced-level SQL and Python for querying and managing structured data within relational database systems (RDBMS).
Designing and implementing data models to enhance efficiency, performance, and fulfill business analytics requirements.
Automating and orchestrating SQL workflows using established software engineering best practices including CI/CD and version control.
Developing standards and practices for data and analytics engineering, focusing on scalable architectural data pipeline patterns.
Utilizing big data platforms including Redshift for efficient large-scale data processing and analysis.
Building and deploying scalable data engineering solutions in cloud environments including AWS using cloud-native tools and services.
Conducting comprehensive quality assurance processes including code reviews, test plan development, and rigorous validation to ensure data accuracy.

Responsibilities

Design and implement data pipeline architecture and processes including sourcing, transformation, and extraction to enable seamless analytics integration.
Utilize expert-level SQL and Python to manage, query, and transform structured data in relational databases to ensure performance, scalability, and maintainability.
Automate and manage robust data pipelines and workflows using software engineering best practices including modular design, version control, and testing frameworks to ensure reliability and repeatability.
Map data between source systems, data warehouses, and data marts in alignment with business requirements, ensuring data consistency across analytical workflows.
Build and maintain data models that enhance efficiency, performance, and support complex business analytics and reporting needs.
Collaborate with analysts, data scientists, and business stakeholders to translate business requirements into actionable, scalable data solutions.
Leverage cloud platforms including AWS, along with big data frameworks like Redshift, to build and scale secure, high-performance data engineering solutions for large-scale analytics.
Develop and enforce standards for development, testing and documentation, including data flow maps to support transparency and maintainability.
Conduct comprehensive quality assurance processes including code reviews, test plan development, and rigorous validation to ensure data accuracy, completeness, and reliability.
Provide troubleshooting support for data pipelines and workflows, focusing on system performance, data integrity, and availability to meet evolving business analytics demands.
Build strong relationships with IT to work on tooling, data strategy, integrations, and deployments.
Stay up to date with the latest analytics engineering trends and emerging technologies, and look for opportunities to improve the stack.