Director of Data Engineering Ruggable - 2.8 Los Angeles, CA Job Details $200,000 - $240,000 a year 11 hours ago Benefits 401(k) Qualifications AI models Roadmap creation (System development task) Data model design Commercial use (data warehousing systems) Cloud analytics services Strategic goal setting Data compliance Data Integration (Data management) Tooling Coaching IT system monitoring IT vision strategy Generative models Managing IT teams Metrics Reporting AI platforms (beyond public GPTs) Scalable systems Talent development Schema design Infrastructure architecture design Team development Hiring Reducing cloud infrastructure costs Key Performance Indicators Data Architecture Design (Architecture design skills) Production systems Cloud engineering team management ETL process automation Machine intelligence Cloud database architecture

Full Job Description About Ruggable:

Ruggable is a leading direct-to-consumer e-commerce brand based in Los Angeles, California with an extraordinary track record of high, profitable growth. We pride ourselves on having an extremely loyal customer base and a talented team made up of genuinely caring people who take action and deliver results. We are venture-backed and own a patented washable rug design that's disrupting the home décor industry. Our mission is to empower our customers to live vibrantly with beautiful products that don't compromise on function. If you're passionate about consumer products, e-commerce, and high-growth start-ups, keep reading!

Job Summary:

Ruggable is looking for a Director of Data Engineering to join our team! Technology is central to everything we do. The Technology team enables the infrastructure that powers our direct-to-consumer and wholesale business across three continents, eleven countries, and seven manufacturing plants, while supporting globally distributed teams. We're looking for someone to lead both the day-to-day operations and the strategic direction of our data platform. This is a hands-on leadership role: you'll run a team of data engineers while also setting the multi-year vision for how our end-to-end data foundation and governance evolves to meet business needs and AI-driven initiatives.

What You'll Do:

People Leadership & Coaching:

Mentor, guide, and develop a team of data engineers, setting clear performance goals, setting engineering standards, unblocking technical hurdles, and fostering engineering excellence

Hands-on Technical Contribution:

Design, review, and optimize scalable data infrastructure, cloud lakehouses, automated data pipelines, and modern SDLC practices (CI/CD, IaC) to support operational reporting and co-work as well as advanced modeling and forecasting Strategic Road mapping: Establish and execute a multi-year data strategy that balances immediate business priorities with sustainable, long-term technical architecture

Self-Service Acceleration:

Meet business users where they are on a varied maturity curve for reporting, and continue to evangelize and build a path toward more consistent, self-serve consumption of data across enterprise reporting and analytics tools

Governance, Security & FinOps:

Establish robust data governance, access controls, and data quality observability (reliability, freshness, volume, anomaly detection) for data products while actively monitoring and optimizing cloud compute and storage spend

Community of Practice:

Establish and evolve engineering practices, standards, and processes as the team and platform scale What You'll Need to

Have:

Required:

8+ years in data engineering or related fields, with 3-4+ years leading, managing, or mentoring engineering teams Bachelor's degree in Computer Science, Engineering, or a related field or equivalent practical experience considered. Track record of building and executing multi-year data strategy and roadmaps, not just managing backlogs. Proven success balancing strategic vision and communication with hands-on code development and architectural design Deep hands-on background in modern data platform architecture: cloud data warehouses/lakehouses (e.g., Snowflake, BigQuery, Databricks, Redshift), data lakes, and lakehouse patterns Strong experience with ELT/ETL orchestration and transformation tooling (e.g., dbt, Airflow, Dagster, Fivetran) Fluency in SQL and at least one programming language commonly used in data engineering (Python, Scala, or Java) Experience designing and scaling semantic layers and BI/consumption layers (e.g., LookML, dbt Semantic Layer, Cube, or similar) that serve varied levels of business user maturity Working knowledge of cloud infrastructure (AWS preferred) Understanding of data modeling best practices (dimensional modeling, Data Vault, or similar) at scale Deep experience building, scaling, and maintaining production-grade machine learning and generative AI pipelines, including model serving infrastructure and feature stores Proven ability to establish comprehensive monitoring for enterprise AI systems, encompassing performance metrics (latency, throughput, cost/token usage), data/concept drift detection, accuracy degradation Demonstrated ability to translate technical strategy and architectural concepts into business terms for executive and cross-functional stakeholders Strong communication skills; comfortable operating across strategic planning and hands-on technical problem-solving Experience hiring, mentoring, and developing engineering talent, including senior/principal-level ICs Track record of managing budgets, vendor relationships, FinOps and cost management for data infrastructure Experience partnering with AI/ML or analytics teams to enable AI-driven use cases on production data infrastructure Working knowledge of data privacy and compliance considerations (e.g., GDPR, CCPA, SOC 2) as they apply to data platforms

Preferred:

Experience building or maturing data governance programs from an early stage, including data cataloging, lineage, quality frameworks, and access controls Infrastructure-as-code practices Familiarity with streaming/real-time data architectures (e.g., Kafka, Kinesis) Understanding of design and delivery of feature stores, vector databases, or retrieval-augmented architectures to support AI-Augmented operational solutions Establishing AI-specific safety metrics (hallucination tracking, bias, toxicity, and guardrail enforcement)

Compensation:

$200,000 - $240,000 per year base salary An annual bonus percentage that varies based on level of role Employer matching (up to 3% of base salary) for company sponsored 401K plan At Ruggable, we offer competitive compensation and benefits packages. Ruggable is an Equal Employment Opportunity employer. We proudly recruit and hire a diverse workforce and are committed to creating an inclusive environment for all employees. If you are based in California, we encourage you to read this important information for California residents linked here.

To all recruitment agencies:

Ruggable does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, Ruggable employees or any other company destination. Ruggable is not responsible for any fees related to unsolicited resumes.