Gibbs Consulting IT Services & Solutions
Senior Data Engineer
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Senior Data Engineer - GCP / BigQuery / Data Products
DURATION: Contract until 31st December 2026, with potential to extend for a further 6 months
LOCATION: London / Hybrid
START DATE: ASAP
ONSITE REQUIREMENTS: Hybrid - 2 days per week onsite in London
ENGAGEMENT TYPE: Outside IR35
DAY RATE / HOURLY RATE: Upto £600 per day (DoE)
LEGAL RIGHT TO WORK: Candidates must have the legal right to work in the UK. Sponsorship is not available for this role.
GIBBS CONSULTING SUMMARY
As part of our continued business growth, we at Gibbs Consulting are expanding our Data Product Feature Team and are looking to bring in an experienced Senior Data Engineer.
You will join our growing consulting team and deliver for an end-customer within the banking sector, supporting a large-scale data platform modernisation programme. This is a hands-on engineering role focused on building scalable, governed and cloud-native data capabilities within Google Cloud Platform.
JOB OVERVIEW
We are looking for a Senior Data Engineer with strong GCP experience to support the design and delivery of a strategic data capability within a major UK banking transformation programme.
You will focus on building scalable data engineering pipelines and engineering patterns that generate, standardise and manage business and technical keys across multiple data products.
Working alongside solution architects, data engineers and platform teams, you will build reliable pipelines, implement deterministic identifier generation and develop matching, merge and data standardisation logic as the end-customer continues its migration from incumbent warehouse platforms to a modern cloud-based architecture.
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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.
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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.
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