The Opportunity

Design, build and operate enterprise-grade data infrastructure, reusable data pipelines and governed data assets that enable analytics, AI and business decision-making across the organization.

The role focuses on cloud-native data engineering using Google Cloud Platform, with strong emphasis on BigQuery, data quality, data governance, observability, performance and security by design.

As part of Technology Architecture, the Data Engineer contributes to shared data capabilities that support Data & AI Tech Factory delivery, business data domains, AI engineering and analytics teams across the enterprise.

What you'll get to do

Strategy & Roadmap

• Contribute to the evolution of the enterprise data platform and data engineering standards.
• Help define reusable patterns for ingestion, transformation, orchestration, monitoring and data productization.
• Support the modernization of analytics and AI data foundations on Google Cloud Platform.
• Promote cloud-first, governed and AI-ready approaches to enterprise data engineering.
• Identify opportunities to reduce duplication and increase reuse across data pipelines, datasets and platform components.

Delivery & Execution

• Design, build and maintain scalable data pipelines and data processing workflows using Google Cloud Platform services.
• Develop BigQuery data models, curated datasets and reusable data layers optimized for analytics and AI consumption.
• Create automated ETL/ELT processes to ingest, clean, enrich and transform data from multiple enterprise and third-party sources.
• Implement batch, near-real-time and event-driven data flows where appropriate, ensuring performance, reliability and operational resilience.
• Support integrations between cloud systems, on-premise systems and third-party applications where data movement or data availability is required.
• Build and optimize data workflows using services such as BigQuery, Cloud Storage, Pub/Sub, Cloud Functions, Cloud Run, Dataflow, Dataproc and Cloud Composer.
• Collaborate with AI Engineering teams to create AI-ready datasets, feature-ready structures and reliable data foundations for ML and GenAI use cases.
• Maintain documentation of data workflows, architectures, data models, dependencies and operational procedures.

Governance & Compliance

• Apply data governance standards for data quality, lineage, metadata, cataloging, documentation and traceability.
• Implement security and privacy controls, including access management, encryption, role-based access and secure data sharing practices.
• Ensure pipelines and datasets comply with internal governance, GDPR and relevant data protection expectations.
• Contribute to observability, monitoring and alerting practices around data pipelines and data platform components.
• Support responsible AI by ensuring that AI-consuming teams rely on trusted, documented, governed and high-quality datasets.

Stakeholder Management

• Collaborate with Miquel Orengo and Technology Architecture stakeholders to align delivery with technical standards and platform priorities.
• Work with Data & AI Tech Factory squads to provide reusable data foundations for domain delivery.
• Partner with Business Data, Consumer Data, Digital Analytics and other data-consuming teams to understand requirements and translate them into scalable technical solutions.
• Coordinate with Integration Engineering when data flows require cross-system connectivity or API-enabled movement.
• Communicate technical constraints and data engineering decisions clearly to both technical and non-technical stakeholders.

Team Leadership

• Act as a strong individual contributor within the Data Engineering capability.
• Share engineering standards, patterns and good practices with peers and delivery squads.
• Support code reviews, design reviews and quality gates where requested by the Data & AI Engineering Manager.
• Mentor junior contributors or external partners when appropriate, without formal people-management responsibility.

Contribute to continuous improvement of development standards, release practices and data engineering maturity.

We'd love to meet you if you have

• 3-6 years of experience in data engineering, analytics engineering, cloud data engineering or enterprise data platform roles.
• Proven hands-on experience building scalable data solutions on Google Cloud Platform, especially with BigQuery.
• Experience designing and operating ETL/ELT pipelines, data models, data warehouses or lakehouse-style architectures.
• Experience with data governance, data quality, metadata, lineage or secure data access practices.
• Experience supporting analytics and AI use cases through trusted datasets and production-grade data flows.
• Experience integrating cloud systems with on-premise or third-party data sources is valuable.

We welcome Creators Of All Kinds. If you are unsure of meeting all the requirements but trust you have the transferable skills to excel in this role, complete the application and our teams will get in touch if you are selected for an interview.

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