At a Glance
- Tasks: Stabilise and structure BigQuery for enterprise-wide data governance and scalability.
- Company: Dynamic company focused on consolidating diverse data sources into a core platform.
- Benefits: Competitive day rate, flexible contract, and opportunity to work in a fast-paced environment.
- Other info: Work 2 days in the office in Central London with a hands-on, delivery-focused approach.
- Why this job: Make a real impact by shaping the future of data architecture in a growing business.
- Qualifications: Strong experience with BigQuery, data modelling, and analytics architecture.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
Senior BigQuery Data Engineer - Contract in London employer: Augustinus Bader
As a Senior BigQuery Data Engineer, you will join a forward-thinking company that values innovation and collaboration in the heart of Central London. With a strong focus on employee growth, the company offers a dynamic work culture that encourages autonomy and creativity, alongside competitive day rates for contractors. You'll have the opportunity to shape the future of data architecture while working with cutting-edge technologies in a supportive environment that prioritises clear communication and sustainable practices.
StudySmarter Expert Advice🤫
We think this is how you could land Senior BigQuery Data Engineer - Contract in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the game. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your BigQuery projects and any cool data models you've built. This is your chance to demonstrate your hands-on experience and make a lasting impression on potential employers.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each role. Research the company and its data needs, then align your skills and experiences with what they’re looking for. This shows you’re genuinely interested and not just sending out cookie-cutter applications.
✨Tip Number 4
Apply through our website! We’ve got a streamlined process that makes it easy for you to showcase your talents. Plus, it gives you a better chance of being noticed by our hiring team. So, don’t miss out – get your application in!
We think you need these skills to ace Senior BigQuery Data Engineer - Contract in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with BigQuery and data governance. We want to see how your skills align with our needs, so don’t hold back on showcasing relevant projects!
Showcase Your Technical Skills:When detailing your experience, focus on your hands-on work with BigQuery and any IaC tools like Terraform. We love seeing specific examples of how you've designed scalable data architectures or improved data ingestion processes.
Keep It Clear and Concise:We appreciate clarity! Use straightforward language and avoid jargon where possible. Remember, your application should be easy to read and understand, especially for those who might not be as technical.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Augustinus Bader
✨Know Your BigQuery Inside Out
Make sure you’re well-versed in BigQuery architecture and data modelling. Brush up on your experience with complex, multi-source data environments, especially in ecommerce and subscription data. Be ready to discuss specific examples of how you've designed and operated BigQuery environments at scale.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've tackled challenges in fast-moving environments. Think of instances where you balanced best practices with pragmatism. They’ll want to see that you can operate with autonomy and constructively challenge existing approaches.
✨Communicate Clearly and Confidently
Since strong communication skills are a must, practice explaining complex concepts in simple terms. You might be asked to describe your previous projects or the technical details of your work, so make sure you can articulate these clearly for both technical and non-technical stakeholders.
✨Prepare for Practical Scenarios
Expect to discuss practical roadmaps for scaling BigQuery usage across different business functions. Think about how you would define and implement golden datasets, and be ready to provide evidence-based views on strengths and gaps in current data engineering capabilities.
