Director of Data Engineering

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