Principal, Engineer - Architecture - Enterprise

Be part of an amazing story

Macy’s is more than just a store. We’re a story. One that’s captured the hearts and minds of America for more than 160 years. A story about innovations and traditions…about inspiring stores and irresistible products…about the excitement of the Macy’s 4th of July Fireworks, and the wonder of the Thanksgiving Day Parade. We’ve been part of memorable moments and milestones for countless customers and colleagues. Those stories are part of what makes this such a special place to work.

Job Overview

The Principal, Data Engineer serves as a strategic leader advancing Macy’s shift to a data-led organization by delivering faster insights that streamline operations and enhance the customer experience. They design and build scalable cloud data platforms that power enterprise-wide analytics and decision-making. They also define data architecture standards in collaboration with enterprise architecture, ensuring alignment with security protocols, governance requirements, and long-term technology strategy.

What You Will Do

• Establish and enforce enterprise-wide data standards and a consistent approach to data modeling aligned with the organization's overall technology strategy.
• Develop and maintain a strategic data roadmap focused on long-term sustainability, scalability, and operational efficiency.
• Collaborate with executive leadership, product teams, and engineering to ensure data initiatives are aligned with business goals and drive measurable value.
• Lead the evaluation and adoption of cutting-edge technologies for data management, storage, and analytics in partnership with data scientists to implement best practices.
• Define, visualize, and document scalable data frameworks to guide data engineers and data scientists in development and maintenance.
• Drive adoption of modern data architecture patterns, including event-driven architectures, real-time data streaming (e.g., Kafka, Pulsar), and AI-powered analytics.
• Leverage AI to analyze data architectures, uncover patterns, identify gaps, and recommend opportunities for improvement.
• Oversee the design, development, implementation, and maintenance of complex data systems, ensuring full lifecycle data management.
• Build and maintain automated, enterprise-grade data pipelines and transformation processes.
• Partner with Legal, Security, and Data Governance teams to ensure compliance and the standardization of certified data assets.
• Mentor and coach engineers on data tools, technologies, and domain expertise to strengthen technical capability across the team.
• Collaborate with business and technology teams to support analytics execution, enhance data accessibility, and enable self-service insights.
• Foster an environment of acceptance and respect that strengthens relationships, and ensures authentic connections with colleagues, customers, and communities. 
• In addition to the essential duties mentioned above, other duties may be assigned.

Skills You Will Need

Cloud Architecture & Big Data Engineering: Designs and implements scalable cloud-based data platforms using technologies like Hadoop, Spark, and modern ETL frameworks to support analytics and enterprise data processing.

Data Strategy & Enterprise Architecture: Establishes enterprise-wide data standards, governance, and compliance frameworks aligned with long-term technology strategy, leveraging tools such as Collibra.

Programming & Data Warehousing: Expert in Python and SQL, with extensive experience building and maintaining data warehouses in both cloud and on-prem environments.

Modern Data Architecture: Leads adoption of event-driven architectures, real-time data streaming technologies (e.g., Kafka, Pulsar), and AI-driven analytics solutions.

Data Engineering & Integration: Architects cloud-native solutions (e.g., GCP) and designs API-first integration using GraphQL, RESTful APIs, or Pub/Sub frameworks.

Pipeline Development & Optimization: Develops and maintains automated, enterprise-grade data pipelines using ETL/ELT processes, with a deep understanding of data modeling, pipeline efficiency, and real-time data ingestion.

Analytics Platform Integration: Experienced in integrating data into business intelligence tools like Tableau and Power BI to enable self-service analytics and data-driven decision-making.

Database Performance Optimization: Skilled in database indexing, partitioning, and storage strategies for relational and NoSQL systems such as PostgreSQL, MySQL, MongoDB, and Cassandra.

Technical Leadership &