Senior Data Engineer (GCP, BigQuery, Looker) [AS233] @ Smart Working — Vaga Remota

Responsibilities

Design, build, and maintain robust ETL/ELT pipelines that move data from source systems into Google BigQuery, ensuring reliability, scalability, and observability at every stage.
Develop and enforce data models and schema standards using best-practice SQL and dimensional modelling principles, with a focus on clarity, reuse, and performance.
Own the Google BigQuery environment, optimising queries, managing costs, enforcing data governance, and ensuring the platform scales alongside the business.
Build and maintain Looker explores, LookML models, and dashboards that translate complex datasets into clear, actionable business intelligence for non-technical stakeholders.
Work across the full Google Cloud Platform stack, including Cloud Storage, Dataflow, Pub/Sub, Cloud Functions, and Composer, to architect end-to-end data solutions.
Partner with analytics, engineering, and commercial teams to understand data requirements and translate business problems into scalable technical solutions.
Champion data quality and testing frameworks, implementing monitoring and alerting so that issues are caught early and resolved quickly.
Contribute to documentation, coding standards, and architectural decision records so the team can move fast with confidence.
Mentor junior data team members and set the bar for engineering rigour across the data function.
Stay current with developments in the modern data stack and proactively recommend tooling or process improvements where appropriate.

5+ years of experience in SQL and data modelling, with strong command of dimensional modelling, star schemas, and performance optimisation.
3+ years working with Google BigQuery in a production environment.
3+ years hands-on experience with Google Cloud Platform (Cloud Storage, Dataflow, Pub/Sub, Cloud Functions, Composer).
3+ years building and maintaining ETL/ELT pipelines at scale.
1+ year working with Looker and LookML to deliver business-facing dashboards and data products.
Demonstrable experience leading at least one data project end-to-end, from scoping through to delivery.
Able to communicate clearly with non-technical stakeholders about data limitations, timelines, and trade-offs.
Comfortable making pragmatic architecture decisions in a cloud-native, modern data stack environment.

Experience with dbt (Data Build Tool) for transformation layer management and testing.
Familiarity with orchestration tools such as Apache Airflow or Cloud Composer.
Python skills for pipeline scripting, data validation, or automation.
Background in retail, ecommerce, or fashion, understanding how data flows across commercial and digital channels.
Exposure to real-time or streaming data pipelines using Pub/Sub or Dataflow.
Experience with Terraform or Infrastructure-as-Code practices in a GCP context.
Familiarity with data governance frameworks, cataloguing, and lineage tracking.