Director of Data Engineering

Mixbook

58d

About The Position

Mixbook is hiring a Director of Data Engineering to own Mixbook’s data foundation end-to-end. This is a hands-on leadership role with high accountability and executive visibility. You will be responsible for the reliability of the data warehouse, ingestion pipelines, and transformations that power analytics and executive reporting. You will lead a small team (2 direct reports) and are expected to personally design, build, and stabilize critical parts of the platform. This person will need to work in the EST time zone.

Requirements

8+ years of experience in data engineering or adjacent roles
Experience as a Senior / Principal Data Engineer, Lead, or hands-on Director
Proven track record building or stabilizing data platforms at mid-scale companies
Project management/backlog management experience. This person will be expected to build a 12 month roadmap and drive prioritization.
Deep experience with: Data warehousing (Redshift or similar) dbt and analytics data modeling Orchestration tools (Dagster, Airflow, etc.) Data ingestion, pipeline reliability, and observability
Comfortable operating with a small team and high accountability
Strong ownership mindset — you feel personally responsible for outcomes
A proven track record of improving data reliability, data quality, and metric consistency in a real business environment
The ability to prioritize ruthlessly and say “no” constructively when tradeoffs are required
Experience working through legacy data systems and incrementally improving them without disrupting the business
Clear, confident communication skills—especially when explaining technical tradeoffs to non-technical stakeholders
An ownership mindset: you take personal responsibility for whether the data platform works or doesn’t

Nice To Haves

Experience in B2C, e-commerce, or marketplace businesses
Familiarity with Looker and semantic modeling
Exposure to ML pipelines or production ML systems

Responsibilities

Own Mixbook’s data platform end-to-end, including ingestion, orchestration, transformation, and the Redshift data warehouse
Establish clear accountability, SLAs, and standards for critical datasets and business metrics
Stabilize and harden fragile pipelines, reducing data incidents and reactive firefighting
Define and enforce best practices for dbt modeling, data quality, and metric consistency. Deliver analytics-ready models that power Looker and downstream use cases.
Partner closely with Platform Engineering, Product Engineering, Data Analytics, and business stakeholders across Marketing, Product, Operations and Finance to align on priorities and tradeoffs
Act as the primary point of contact for data reliability and warehouse health at the executive level
Make pragmatic build vs. buy decisions and resist unnecessary replatforming or tool churn
Lead by building. Write and review production data engineering code. Set technical standards and architectural direction.
Enable faster, more confident analytics and experimentation by increasing trust in data
Set the stage for future data science, ML and AI capabilities once foundations are solid