Senior BigQuery Data Engineer at Augustinus Bader, London, 4-5 Months, £Day Rate

Role title

Senior BigQuery Data Engineer / Contract

Contract type

Day rate contractor

Initial term four to five months

Context

The business is consolidating a growing number of data sources into BigQuery as a core enterprise data platform. Initial focus has been on DTC and ecommerce, with planned expansion across finance, operations, logistics, marketing and others.

Current data sources include ecommerce platforms, subscription systems, customer service tools, personalisation platforms, and marketplace integrations. Data is actively consumed via SQL and AI assisted analysis to power internal reporting applications built in Laravel.

The role is required to stabilise, structure, and future proof the BigQuery environment so it can support scale, governance, and enterprise wide adoption. Preference for IaC, such as Terraform

Primary objectives

• BigQuery architecture and data model ownership
• Review the current BigQuery structure, ingestion patterns, and table design.
• Design and implement a scalable, well governed data architecture suitable for a global enterprise business
• Define and implement golden datasets with clear ownership, access rules, and change control.
• Introduce appropriate schema and field level controls to prevent uncontrolled changes and data drift.
• Ensure the data model supports downstream analytics, AI driven querying, and application level reporting.
• Produce clear documentation explaining the architecture, data model, and usage patterns for both technical and non technical stakeholders.
• Delivery oversight and operating model support
• Work alongside the existing data engineering resource to review current data pipelines, models, and delivery practices.
• Assess the effectiveness of current ways of working, technical approaches, and delivery processes against current and future business needs.
• Provide an evidence based view on strengths, gaps, and areas for improvement across data engineering capability and operating model.
• Make pragmatic recommendations on role scope, process improvements, upskilling opportunities, and resourcing required to support the target state.
• The required tech stack is Google Big Query plus ‘Infrastructure as Code’ / DBT to be confirmed.

Key deliverables

• Documented target state BigQuery architecture and data model.
• Defined and implemented golden tables with clear ownership and governance.
• Standards for data ingestion, transformation, and consumption.
• A practical roadmap for scaling BigQuery usage across additional business functions.
• Clear documentation that enables confident use of data across the organisation.

Required experience

• Strong hands on experience designing and operating BigQuery environments at scale.
• Deep understanding of data modelling, analytics architecture, and data governance.
• Experience working with complex, multi source data environments, ideally including ecommerce and subscription data.
• Experience with data pipeline orchestrations tools such as Cloud Composer, Airflow or equivalent.
• Comfort working in fast moving environments with imperfect starting points.
• Ability to balance best practice with pragmatism and delivery speed.
• Strong communication skills and ability to explain complex concepts clearly.

Nice to have

• Experience supporting AI driven analytics or natural language querying of data.
• Experience working closely with application teams consuming data directly in products or dashboards.
• Background in DTC, retail, or consumer brands.

Working style

• Hands on and delivery focused.
• Pragmatic and outcome driven.
• 2 Days in office (Central London) per week.
• Comfortable operating with autonomy.
• Able to challenge existing approaches constructively.
• Focused on clarity, documentation, and long term sustainability.

Success looks like

• BigQuery is trusted as a scalable, governed enterprise data platform.
• Golden datasets (Curated, business validated tables that serve as the single source of truth) are clearly defined, locked down, and actively used.
• The business is unblocked to expand data usage across finance, operations, and other functions.

There is clear visibility on the current operating model and what is required to support future growth.