Note: The job is a remote job and is open to candidates in USA. Deckers Brands is an organization focused on delivering exceptional business results while fostering an inclusive and supportive workplace. The Data Engineering Lead provides senior technical leadership across enterprise data products, establishing engineering standards, leading analytics-ready data model and AWS pipeline delivery, coaching distributed teams, and driving data quality and reliability.
Responsibilities
- Provide day-to-day technical leadership for data engineering design, build quality, and delivery standards across enterprise data products
- Review designs and enforce coding standards across internal and partner engineering, acting as the technical review gate before build begins on significant work
- Own engineering standards for model design and naming, code review, CI/CD, testing, documentation, and deployment practices across distributed teams
- Lead the design and delivery of analytics-ready data models and transformation layers using dbt, including staging, intermediate, and mart layers with tests, documentation, and governed release workflows
- Build and operate cloud-native ingestion, transformation, and delivery pipelines on AWS, focusing on reliability, performance tuning, and cost efficiency
- Establish and maintain reusable data engineering patterns for ingestion, transformation, and delivery so that solutions are repeatable
- Provide technical direction across distributed engineering teams, including external delivery partners, and hold the technical quality of their output to Deckers standards
- Coach and develop data engineers and analytics engineers through design review, pairing, technical guidance, and structured knowledge transfer
- Reduce key-person dependency by documenting engineering patterns, designs, and runbooks so that critical knowledge is retained by the organization
- Drive data quality practices within engineering, including source definitions, freshness monitoring, automated tests, reconciliation, and lineage expectations
- Lead root cause analysis and remediation for complex production data issues, strengthening patterns and controls
- Translate business needs from finance, sales, supply chain, product, and consumer stakeholders into scalable data engineering designs
- Partner with data architecture, cloud engineering, and security to align data product designs with enterprise architecture, security, privacy, and compliance requirements
Skills
- Interested applicants must reside in one of the following approved states: Arizona, California, Colorado, Indiana, Massachusetts, Minnesota, New York, Oregon, Pennsylvania, Texas, Utah, Washington
- Bachelor's degree required, preferably in Computer Science, Computer Engineering, or a related technical field
- 8–10 years of experience building enterprise-grade data platforms, pipelines, and analytics-ready data models
- 3+ years providing technical leadership on data engineering delivery, including design review and standards enforcement
- Hands-on experience using dbt as a primary transformation framework in production, including model design, testing, documentation, CI/CD, and release practices
- Strong experience delivering data pipelines on AWS (e.g., S3, Redshift, Glue, Athena, Lambda) and cloud data warehouse platforms
- Experience providing technical direction to distributed engineering teams, including external delivery partners
- Experience with data quality practices including automated testing, reconciliation, freshness monitoring, and lineage
- Deep expertise in dimensional modeling and analytics engineering, including dbt best practices and SQL performance tuning
- Strong AWS data engineering skills including pipeline reliability, performance tuning, and cost awareness
- Ability to set and hold engineering standards across teams the role does not directly manage
- Strong design review, technical coaching, and knowledge transfer skills
- Ability to translate ambiguous business requirements into scalable and repeatable engineering designs
- Proficiency in SQL and Python, with working knowledge of orchestration tooling such as Airflow
- Familiarity with AI-assisted development workflows and their application to pipeline development, refactoring, and documentation
- Excellent problem-solving and root cause analysis skills
- Strong communication skills across technical and business stakeholders
- Comfortable working in a fast-paced, matrixed, and global environment
- Master's degree preferred
- Experience supporting machine learning and advanced analytics use cases through curated, feature-ready datasets is preferred
Benefits
- Competitive compensation programs and bonuses
- Financial planning and wellbeing plans to protect income, pay for expenses, and invest in the future
- Time away from work to be with family, focus on health, or recharge
- Generous discounts and community-based programs
- Extensive opportunities and support for personal and professional development
- Comprehensive health and wellness programs and offerings
Company Overview
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