Data Engineering Lead

This is a fully remote position, open to applicants in Arizona, +10 more states.

📋 Description

• Deliver day-to-day technical leadership for the design, quality, and delivery standards of enterprise data engineering products.

• Evaluate designs and uphold coding standards for both internal and partner engineering, serving as the technical review gate before major work commences.

• Own the engineering standards related to model design and naming, code reviews, CI/CD, testing, documentation, and deployment methodologies.

• Guide the design and execution of analytics-ready data models and transformation layers utilizing dbt.

• Construct and maintain cloud-native ingestion, transformation, and delivery pipelines on AWS.

• Create and uphold reusable data engineering patterns for ingestion, transformation, and delivery processes.

• Provide technical guidance across distributed engineering teams and external delivery partners.

• Mentor and develop data engineers and analytics engineers through design reviews, collaborative work, technical advice, and knowledge sharing.

• Document engineering patterns, designs, and runbooks to mitigate key-person dependency.

• Promote data quality practices encompassing source definitions, freshness monitoring, automated testing, reconciliation, and lineage.

• Lead root cause analysis and resolution for intricate production data challenges.

• Convert business requirements from finance, sales, supply chain, product, and consumer stakeholders into scalable data engineering solutions.

• Collaborate with data architecture, cloud engineering, and security to ensure designs align with enterprise architecture, security, privacy, and compliance mandates.

⛳️ Requirements

• A Bachelor’s degree is required, ideally in Computer Science, Computer Engineering, or a related technical area.

• 8–10 years of experience in developing enterprise-grade data platforms, pipelines, and analytics-ready data models.

• Over 3 years of experience providing technical leadership in data engineering delivery, including design reviews and standards enforcement.

• Practical experience using dbt as a primary transformation framework in production, covering model design, testing, documentation, CI/CD, and release practices.

• Extensive experience in delivering data pipelines on AWS and cloud data warehouse platforms.

• Proven experience in providing technical direction to distributed engineering teams, including external delivery partners.

• Familiarity with data quality practices such as automated testing, reconciliation, freshness monitoring, and lineage.

• Preferred experience in supporting machine learning and advanced analytics use cases through curated, feature-ready datasets.

• Profound knowledge in dimensional modeling and analytics engineering, including dbt best practices and SQL performance tuning.

• Strong AWS data engineering competencies, including pipeline reliability, performance optimization, and cost management.

• Ability to establish and uphold engineering standards across teams that the role does not directly supervise.

• Exceptional skills in design review, technical coaching, and knowledge transfer.

• Competence in translating ambiguous business requirements into scalable and repeatable engineering designs.

• Proficiency in SQL and Python, along with a working knowledge of orchestration tools like Airflow.

• Familiarity with AI-assisted development workflows and their application in pipeline development, refactoring, and documentation.

• Excellent problem-solving abilities and root cause analysis skills.

• Strong communication skills with both technical and business stakeholders.

• Comfortable operating in a fast-paced, matrixed, and global environment.

• Must reside in one of the approved states: Arizona, California, Colorado, Indiana, Massachusetts, Minnesota, New York, Oregon, Pennsylvania, Texas, Utah, or Washington.

🏝️ Benefits

• Competitive compensation packages and bonuses.

• Financial planning and well-being programs.

• Income protection, expense support, and investment plans.

• Time-away-from-work programs.

• Generous discounts.

• Community-based initiatives.

• Opportunities and support for personal and professional development.

• Comprehensive health and wellness programs and offerings.