Deckers BrandsRemote
Sr. Manager, Data & ML Engineering
United States of America
Full Time
2 weeks ago
$170,000 - $190,000 USD
H1B Sponsor Likely
Key skills
Data engineeringMachine learningdbtAWSAWS S3AWS RedshiftAWS GlueAWS EMRAWS LambdaAWS KinesisAWS SageMakerData modelingAnalytics engineeringCI/CDData pipeline developmentData quality managementData governanceMLOpsCoachingAnalytical thinkingInfluencingDecision makingMachine LearningMLData EngineeringAnalyticsRedshiftLambdaS3GlueSageMakerKinesisLeadershipCommunicationCollaboration
About this role
Deckers Brands is committed to creating an inclusive workplace where employees can thrive. The Senior Manager, Data & ML Engineering will lead the delivery and operational excellence of the company's modern data platform, focusing on building scalable data pipelines and enabling machine learning capabilities in collaboration with various teams.
Responsibilities:
- 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)
- 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
- Experience supporting ML initiatives through strong data foundations, feature readiness, and platform enablement is preferred