Director of Data Engineering

Ruggable•Los Angeles, CA

About The Position

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.

Requirements

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

Nice To Haves

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)

Responsibilities

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