• 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.