Data Engineering Lead

Deckers Brands•Washington, DC

7d•$122,300 - $165,000•Remote

About The Position

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 in order for us to bring our different perspectives to work, we are truly Better Together.

Requirements

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.
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.

Nice To Haves

Experience supporting machine learning and advanced analytics use cases through curated, feature-ready datasets is preferred.

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.