Our hiring partner, a premier portfolio of globally recognized lifestyle brands, is seeking a Senior Data Engineer to join their Enterprise Data Warehouse team during a pivotal phase of modernization. As the organization transitions from legacy ETL frameworks to cloud-native architectures, they are looking for a builder who can bridge the gap between established data foundations and the future of AI-augmented engineering.

In this role, the individual will be a key player in modernizing data pipelines, moving toward high-performance, modular code that scales with the business. Collaborating with a deeply experienced team of engineers and managers, this engineer will deliver complex projects across Marketing and Merchandising, ensuring the data ecosystem is scalable, observable, and ready for the next generation of retail analytics.

The ideal candidate is a seasoned data engineer with deep cloud expertise, a passion for modernization, and a commitment to operational excellence. They thrive on tackling complex migration challenges, establishing technical standards, and leveraging AI tools to accelerate development while maintaining rigorous quality standards.

Role Responsibilities

1. Modernize & Migrate: Lead the technical refactoring of legacy data pipelines into modern, cloud-native architectures within Snowflake and Google BigQuery, ensuring performance optimization and scalability.
2. Technical Standards: Establish and champion best practices for source control (Git), branching strategies, and CI/CD patterns within the Data Engineering team, driving consistency and quality across all development efforts.
3. Pipeline Engineering: Design and implement robust, maintainable data pipelines supporting merchandising analytics, web analytics, multi-touch attribution (MTA), and comprehensive customer journey mapping.
4. Operational Excellence & On-Call Support: Participate in a rotating on-call schedule to provide support for EDW Data Engineering owned assets and processes. Respond to alerts from Operations, troubleshoot and diagnose technical issues, coordinate with internal stakeholders and vendors, and manage escalations to ensure timely resolution.
5. AI-Augmented Development: Leverage AI coding assistants (e.g., Claude Code, Snowflake Cortex) to accelerate development cycles, enhance documentation quality, and streamline the refactoring of legacy codebases while maintaining rigorous quality standards.
6. Data Quality & Observability: Implement advanced Snowflake telemetry and data quality management functions to ensure pipeline reliability, enabling proactive error detection and rapid incident response.
7. Collaborative Modeling: Partner with Senior Engineers to implement "Modeling-as-Code," ensuring new data structures follow established dimensional modeling standards while remaining modular, testable, and well-documented.
8. Source Integration: Navigate complex integrations across a diverse source landscape, including Warehouse Management Systems, Sterling Order Management, Island Pacific Merchandising Systems, and POS systems, ensuring seamless data flow across the enterprise.
9. Offshore Collaboration: Work closely with offshore engineering partners to provide technical guidance and ensure consistency across global development efforts, fostering a unified approach to data engineering.

Role Qualifications

Must-Have

1. Experience: 6-8 years of experience in data engineering, with a proven track record of modernizing data pipelines in cloud environments (Snowflake or BigQuery).
2. Modern Tooling: Proficiency in modern version control (Git) and CI/CD workflows. While the organization currently uses Skybot for orchestration, familiarity with DAG-based tools (Airflow, Dagster, or similar) is a plus.
3. Advanced SQL: Expert-level SQL skills with the ability to write modular, performant, and maintainable code. Experience with modular SQL transformation patterns is highly preferred.
4. Domain Expertise: Previous experience with retail or e-commerce data models, specifically in web analytics pipelines and attribution logic, with an understanding of the unique challenges in these domains.
5. Cloud Mastery: Deep, hands-on experience with Snowflake (including telemetry and administrative functions) and/or Google Cloud Platform (BigQuery), with a strong understanding of cloud-native data architectures.
6. AI Literacy: Comfortable using AI assistants to optimize workflows and generate boilerplate code, with the ability to critically judge and audit AI-generated outputs for correctness, security, and performance.
7. Communication: Excellent communication skills with the ability to explain complex technical migrations to stakeholders and collaborate effectively in a "team sport" modeling environment.

Nice-to-Have

1. Experience with DAG-based orchestration tools such as Airflow or Dagster.
2. Familiarity with additional cloud platforms and data tools beyond Snowflake and BigQuery.
3. Experience with data modeling tools and frameworks that support modular, testable data transformations.

The Perks

Our client offers a comprehensive suite of Perks & Benefits to all eligible employees. Availability and eligibility may vary based on employment status and location, but generally include competitive medical, dental, and vision coverage, generous PTO, substantial employee discounts, and robust retirement savings plans.