Data Architect & Engineering Lead

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