Senior Data Engineer - GCP/BigQuery/Data Products

Responsibilities

• Design and develop scalable data engineering components supporting enterprise key management across GCP-based data products.
• Build pipelines and transformation logic for candidate keys, record matching, merge, cleansing and data standardisation.
• Implement processes for generating and maintaining surrogate keys, deterministic UUIDs and master keys.
• Integrate key generation processes into BigQuery-based data product stores.
• Develop and optimise transformation logic using dbt, Dataflow, Dataproc and BigQuery SQL.
• Develop, test and maintain production-grade code supporting data pipelines and reusable engineering components.
• Implement data protection controls including masking, obfuscation, tokenisation and pseudonymisation of sensitive identifiers.
• Collaborate with solution architects, data architects, engineers and platform teams to deliver aligned engineering solutions.
• Support testing and validation across data quality, key generation, interoperability, lineage and operational resilience.
• Produce technical documentation including pipeline designs, implementation standards and operational runbooks.
• Work within the governance, change and release processes required within a regulated banking environment.

Requirements

• 5+ years of professional Data Engineering experience within enterprise data environments.
• Strong hands-on experience with Google Cloud Platform, particularly BigQuery, Dataflow, Dataproc and Cloud Storage.
• Strong hands-on coding experience, ideally using Python, with the ability to develop, test and maintain production-grade data engineering pipelines and reusable components.
• Experience working within banking or regulated financial services.
• Strong SQL engineering skills and experience developing transformations for large-scale structured datasets.
• Hands-on experience with dbt or a similar transformation framework.
• Experience building pipelines involving record standardisation, matching, merge logic and identity resolution.
• Strong understanding of surrogate keys, business keys, deterministic identifiers and data platform key management.
• Experience handling sensitive data using controls such as masking, hashing, tokenisation or pseudonymisation.
• Experience implementing data quality, lineage and traceability controls.
• Understanding of batch and event-driven processing, including monitoring, recovery and operational resilience.
• Strong communication skills with the ability to work effectively across engineering, architecture and platform teams.

Nice to Have

• Experience delivering data platform modernisation programmes or data product-oriented architectures.
• Understanding of Data Mesh or product-aligned data ownership models.
• Experience integrating data platforms with microservices architectures.
• Experience with orchestration tools such as Airflow, Control-M or similar.
• Experience with data governance and metadata tooling such as Dataplex, Collibra or equivalent.
• Experience migrating Legacy warehouse platforms to cloud-native data platforms.