15 set - Milano
Motork
Overview
In this role you will own and shape MotorK's data platform, building data models, pipelines and warehouse architecture while guiding a small two-person data team. You will set technical direction, maintain high engineering standards, and drive modern data practices across the platform. You’ll apply agentic AI concepts to automate workflows and enable scalable data solutions for our automotive SaaS business. This is a hands-on, impact-driven leadership position with cross-functional collaboration.
Retribuzione / Benefits
- salary from 80,000/year
- meal vouchers
- 26 days holiday + national holidays
- 30 days/year to work from anywhere
- flexible hybrid setup
- room to grow nationally and internationally
Responsabilità
- Own end-to-end data modeling architecture across the platform for scalability, performance and maintainability
- Define the target data infrastructure architecture and the roadmap to reach it (warehouse, transformation, orchestration)
- Introduce modern data engineering patterns and governance for the data estate
- Manage and optimize cloud data warehouse performance, cost and reliability
- Design and build production-grade pipelines with dbt, Airflow and Airbyte, with hands-on ownership
- Write production Python for pipelines, tooling and automation
- Architect support for both batch and streaming use cases (Kafka or similar)
- Incorporate agentic AI into data platform architecture and operations
- Lead, mentor and set technical direction for a 2-person data team through reviews and pairing
- Raise quality standards: data quality, testing, documentation and delivery predictability
- Collaborate with product, engineering and business stakeholders to translate requirements into robust data solutions
Requisiti fondamentali
- Deep hands-on expertise in data modeling and data architecture
- Production experience with cloud data warehouses (BigQuery, Redshift, Snowflake or equivalent)
- Hands-on experience with dbt, Airflow and Airbyte in production
- Strong Python skills for data engineering pipelines and tooling
- Familiarity with modern data engineering concepts and AI trends
- Kafka or other streaming technologies is a plus
- Proven ability to lead engineers technically while remaining hands-on
- Clear communicator with technical and non-technical stakeholders
- Bias to action and fast iteration in the face of incomplete information
- communication
- mentorship and leadership
- cross-functional collaboration
- Cloud data warehouse expertise (BigQuery, Redshift, Snowflake)
- DBT
- Airflow
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