Job description

At Deckers Brands, Together, Every Step is a promise kept that every employee can bring their authentic self, is valued and supported, as a whole person, at work and beyond. Together, Every Step is how we continue to deliver exceptional business results, experience an amazing place to work, and have a positive impact on the communities and world around us. Job Title: Data Engineering Lead Reports to: Sr. Manager, Data & ML Engineering Location: US (Remote) Interested applicants must reside in one of the following approved states: Arizona, California, Colorado, Indiana, Massachusetts, Minnesota, New York, Oregon, Pennsylvania, Texas, Utah, Washington The Role As the Data Engineering Lead, you will be the senior technical voice within Deckers’ data and ML engineering organization. You’ll provide hands-on leadership for data engineering design, build quality, and delivery standards across our enterprise data products. This role is all about setting the bar for technical excellence—establishing design patterns, coding standards, and review gates for distributed engineering teams, including external partners. You’ll build engineering capability through design review, pairing, coaching, and structured knowledge transfer, ensuring our data products are reliable, scalable, and ready to power business decisions. We celebrate diversity--of your background, your experiences and your unique identity. We are committed to ensuring an inclusive and equitable workplace where all of our employees can Come as They Are. We believe that when we bring our different perspectives to work, we are truly Better Together. Your Impact 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. Who You Are Bachelor’s degree required, preferably in Computer Science, Computer Engineering, or a related technical field; Master’s degree preferred. 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. Experience supporting machine learning and advanced analytics use cases through curated, feature-ready datasets is preferred. 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. What We ll Give You – Competitive Pay and Bonuses - We’ve created a variety of competitive compensation programs to foster career development, reward success and to show our employees just how much they’re valued. Financial Planning and wellbeing - No matter what financial goals our employees have set, we want to help them get there. Our plans provide powerful ways to protect income, pay for expenses and invest in the future. Time away from work - Sometimes we need time away to be with family, focus on our health or just simply recharge. Our plans support our employees’ needs to get out, get healthy and come back stronger than ever. Extras, discounts and perks - Being a valued member of the Deckers Brands team means more than just a paycheck. From generous discounts to community-based programs, we offer a variety of cool extras Growth and Development - Deckers Brands was built on the idea of pursuing passion. That’s why we offer extensive opportunities and support for personal and professional development. Health and Wellness - There’s nothing basic about our comprehensive health and wellness programs and offerings. While at work and at play, we aim to support a healthy lifestyle. $122,300.00 - $165,000.00 The salary range posted reflects the estimated minimum and maximum target for new hire salaries for this role in: Goleta, California (Remote) Individual pay will be determined by location and additional factors, including job related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary for your preferred location during the hiring process. Equal Employment Opportunity Diversity and inclusion are key to our success. We are proud to be an equal opportunity employer, and our employees are people with different strengths, experiences and backgrounds who share a passion for our brands. We welcome qualified applicants regardless of their race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, age, military or veteran status, mental or physical disability, medical condition and all the other beautiful parts of your identity.

Requirements

Must-haves

Job Title: Data Engineering Lead
Reports to: Sr. Manager, Data & ML Engineering
Location: US (Remote)
Interested applicants must reside in one of the following approved states: Arizona, California, Colorado, Indiana, Massachusetts,
Minnesota, New York, Oregon, Pennsylvania, Texas, Utah, Washington
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.
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.

Nice-to-haves

Bachelor’s degree required, preferably in Computer Science, Computer Engineering, or a related technical field; Master’s degree preferred.
Experience supporting machine learning and advanced analytics use cases through curated, feature-ready datasets is preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlci/cdawsmachine learningsqlpythonai

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Wellness

Professional Development