Senior Manager, Data & ML Engineering

🕒 January 31

🌵 Arizona, California, +9 more states – Remote

🌵 Arizona – Remote 🏄 California – Remote ⛰️ Colorado – Remote 🗽 New York – Remote 🌲 Oregon – Remote 🍂 Massachusetts – Remote ❄️ Minnesota – Remote 🔔 Pennsylvania – Remote 🤠 Texas – Remote ⛷️ Utah – Remote ☕ Washington – Remote

💵 $170k - $190k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Deckers Brands

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1973

👥 B2C

👗 Fashion

🛒 Retail

B2C • Fashion • Retail

Deckers Brands is a global leader in designing, marketing, and distributing innovative footwear, apparel, and accessories. Known for its high-quality products and trendsetting designs, Deckers Brands encompasses a portfolio of well-known brands that appeal to diverse consumer segments worldwide.

📋 Description

• Lead the design and delivery of analytics-ready data models and transformation layers using dbt as the standard framework • Establish and enforce dbt development standards, including model design, documentation, testing, CI/CD, and release practices • Own delivery and operations of scalable ingestion, transformation, and delivery pipelines on AWS, ensuring reliability, performance, and cost efficiency • Partner with Cloud Engineering and Security to ensure AWS data solutions meet security, privacy, and compliance requirements • Implement monitoring, alerting, incident response practices, and runbooks for dbt and AWS workloads to improve operational stability • Drive strong data quality practices including source definitions, freshness checks, automated tests, and data lineage expectations • Collaborate with business stakeholders to translate needs into prioritized roadmaps and delivered data products • Manage and mentor data engineers and analytics engineers through coaching, performance management, and career development • Promote disciplined engineering practices across the team including code review standards, documentation, automation, and reusable frameworks • Enable future machine learning use cases by ensuring curated datasets are ML-ready, including feature readiness and foundational requirements for model operationalization • Evaluate and introduce platform improvements that strengthen scalability, maintainability, governance, and developer productivity

🎯 Requirements

• Bachelor’s degree required, preferably in Computer Science, Engineering, or related technical field; Master’s degree preferred • AWS certifications (Data, Machine Learning, or Solution Architecture) are a plus • 8 to 12 years of experience building enterprise-grade data platforms and pipelines • 3 to 5+ years leading data engineering and/or analytics engineering teams in cloud-native environments • Demonstrated hands-on experience using dbt as a primary transformation framework in production, including testing, documentation, CI/CD, and release practices • Strong experience delivering data platforms on AWS (S3, Redshift, Glue, EMR, Lambda, Kinesis, SageMaker as applicable) • Experience supporting ML initiatives through strong data foundations, feature readiness, and platform enablement is preferred • Deep understanding of modern data modeling and analytics engineering concepts, including dbt best practices • Strong AWS data engineering expertise including scalability, reliability, and cost optimization • Strong leadership and people-management skills with a focus on coaching and developing talent • Ability to drive technical excellence while balancing speed, quality, and operational stability • Excellent problem-solving, analytical thinking, and decision-making skills • Strong communication and influencing skills across technical and business stakeholders • Comfortable working in a fast-paced, matrixed, and global environment

🏖️ Benefits

• Competitive Pay and Bonuses • Financial Planning and wellbeing • Time away from work • Extras, discounts and perks • Growth and Development • Health and Wellness