Data Architect

Responsibilities

• Act as a hands-on data architect, directly shaping and implementing scalable, secure, and high-performing data solutions across Google Cloud Platform (GCP) and hybrid environments;
• Lead the end-to-end design and modeling of data architectures, translating strategic business goals into actionable technical roadmaps and robust, well-documented designs;
• Collaborate closely with engineering teams to build and optimize data models, pipelines, and storage solutions using modern technologies like dbt, Google Cloud Pub/Sub, Dataflow, Cloud Composer, Dataproc, and Dataform;
• Articulate and enforce the impact of architectural choices on non-functional requirements such as scalability, performance, availability, and security;
• Provide technical guidance and mentorship to development teams on data modeling, integration, and performance, fostering a culture of engineering excellence;
• Construct, advocate for, and drive the adoption of architectural principles, standards, and reference patterns at a company-wide level;
• Support cloud migration strategies with a sharp focus on data transformation, optimization, and ensuring long-term architectural consistency;
• Continuously research and evaluate emerging technologies and industry trends to inspire and drive architectural improvements both within and beyond your immediate area of responsibility;
• Inform and influence the technology evaluation process, applying a deep understanding of large-scale computing solutions and enterprise technology trends.

Qualifications

• Proven experience as a hands-on Data Architect or in a similar senior engineering role with a track record of designing and implementing large-scale data solutions;
• Deep, hands-on mastery of Google Cloud Platform (GCP), especially Google BigQuery (advanced modeling, cost optimization, performance tuning, and orchestration);
• Extensive knowledge of diverse database technologies (relational, NoSQL) and modern data paradigms such as Data Mesh, Data Fabric, and Lakehouse;
• Strong hands-on proficiency in Python, with a commitment to applying Clean Code principles and software engineering best practices;
• Experience with the modern Google Cloud data stack, including technologies like dbt, Cloud Pub/Sub, Dataflow, Cloud Composer, Dataproc, and Dataform;
• A strong track record in engineering robust data solutions across both streaming and batch paradigms, particularly leveraging serverless technologies;
• Demonstrated ability to lead and collaborate effectively within cross-functional, globally distributed teams;
• Excellent communication skills in English (both written and verbal), with the ability to convey complex technical concepts with clarity to both technical and non-technical audiences.