Data Architect & Engineering Lead

MotorK sits at the intersection of deep vertical complexity - automotive SaaS, 10,000+ dealerships, enterprise-grade requirements - and a company that has gone all-in on AI as its primary engine of scale. We believe vertical SaaS captures the most value from AI, and none of that value is reachable unless the data underneath it is modeled, governed and engineered right.

The Data Architect & Engineering Lead owns the architecture, engineering practice and delivery of MotorK's data platform. This is a hands-on, build-and-lead role: you will design and build the data models, pipelines and warehouse architecture yourself, while setting technical direction and raising the bar for a small team of two data engineers. You inherit a real platform and a real team, and you are expected to raise the ceiling on both.

•      Deep, hands-on expertise in data modeling and data architecture.

•      Strong production experience with a cloud data warehouse: BigQuery, Redshift, Snowflake or equivalent.

•      Top-tier, hands-on experience with dbt, Airflow and Airbyte in real production environments.

•      Strong Python skills applied to data engineering pipelines and tooling.

•      Familiarity with modern data engineering concepts, with a genuine interest in where the field - including agentic AI - is heading.

•      Kafka or other streaming technologies is a strong plus.

•      Proven ability to technically lead engineers - through mentorship, code review and architectural decision-making - while remaining hands-on.

•      Clear communicator able to work with both technical and non-technical stakeholders.

•      Bias to action - moves fast on incomplete information, ships and iterates rather than waiting for the perfect plan.

•      Own end-to-end data modeling architecture across the platform, balancing scalability, performance and maintainability.

•      Define and evolve the target architecture for data infrastructure - warehouse, transformation and orchestration layers - and the roadmap to get there.

•      Bring modern data engineering practices and patterns into how MotorK designs, builds and governs its data estate.

•      Manage and optimize the cloud data warehouse (BigQuery, Redshift, Snowflake or equivalent), owning performance, cost and reliability.

•      Design and build production-grade pipelines using dbt, Airflow and Airbyte, with TOP-tier hands-on ownership of these tools.

•      Write production Python for pipeline development, tooling and automation.

•      Architect infrastructure that supports both batch and, increasingly, streaming use cases (Kafka or equivalent stream technologies a plus).

•      Bring agentic AI concepts into how the data platform is architected and operationalized - from pipeline automation to data-driven agent use cases.

•      Evaluate where AI-assisted tooling can remove manual, repetitive work from the data engineering lifecycle.

•      Lead, mentor and set technical direction for a team of 2 data engineers - hands-on code review, pairing and architectural guidance.

•      Raise the bar on engineering standards: data quality, testing, documentation and delivery predictability.

•      Partner cross-functionally with product, engineering and business stakeholders to translate requirements into robust data solutions.