Omaze
Charity
StaffDataEngineer
Neural analysis suggests this role is
optimal for Staff candidates.
“Staff Data Engineer at Omaze. Skills: Data Engineering, Data Pipelines, Data Warehousing, Data Quality, Observability, ML Workflows. Designing pipelines. Modelling foundational datasets”
What You'll Achieve.
Create leverage for analytics engineers, data scientists, and stakeholders; Ensure decision-critical datasets are trustworthy; Ensure problems are easy to find and fix; Ensure data flowing into our platform is trustworthy by design; Ensure appropriate, compliant data is reliably shared with external sources; Make the cost of running our data platform visible and well understood; Ensure data collection, storage, and usage is responsible, secure, and meets regulatory bar
Industry & Context.
Make problems easy to find and fix when they happen; Interrogate AI output; Review AI-generated code with rigour
What They're Looking For.
Must Have
Extensive data engineering experience, Extensive experience across industry-standard data warehousing software (e.g. Databricks, Snowflake, etc.), Extensive experience across server-side tech (e.g. Typescript, DynamoDB, AWS Lambda, API Gateway and AWS CDK), Hands-on experience with modern orchestration tooling (Aiflow, Dagster, etc.), Hands-on experience with ingestion tooling such as Fivetran, Experience building out custom scripts when the right off-the-shelf connector doesn’t exist, A track record of building data quality, testing, and observability into platforms, An understanding of data architecture strategies that can scale with the business for the next 4 years, Experience supporting ML workflows / laying the data foundations for them, The ability to bar-raise through code review, mentorship, and the standards you set
Nice to Have
AI-assisted and agentic coding
What You'll Do.
Modelling foundational datasets
Embedding quality and observability into infrastructure
Build and maintain robust ingestion and processing pipelines
Build the data quality
and lineage frameworks
Work with engineers across the organisation to ensure events are properly constructed
Ensure that data pipelines to external sources are reliably sharing appropriate
Lay the foundations to support data science and ML work
Make the cost of running our data platform visible and well understood
Take responsibility for data governance and compliance
Use AI-assisted and agentic coding tools
How You'll Work.
Team & Collaboration
Work with engineers across the organisation; Create leverage for analytics engineers, data scientists, and stakeholders
Full Job Description
🌍 Location: Holborn, London ⭐ Office Policy: 3 days in person, 2 days at home each week Who We Are: At Omaze, we give our community in the UK and Germany the chance to win luxury homes and other life-changing prizes — all while raising money for the causes they love. Thanks to our Omaze Community, we’ve raised over £100 million for UK charities in just five years. That’s millions helping organisations like Age UK, the RSPCA, British Heart Foundation and Great Ormond Street Hospital Charity deliver life-saving work. And the best part? We’re only just getting started. Omaze is building a business and culture committed to growth and creating significant social impact on a global scale. 🔑 ABOUT THE JOB As a Staff Data Engineer at Omaze, you will be a key member of Omaze’s lean data function. Reporting into the Director of Data Platforms, you're at your best designing pipelines, modelling foundational datasets, and embedding quality and observability into infrastructure that people genuinely trust. You bring strong judgement to architectural decisions, raise the bar through example, and create leverage for analytics engineers, data scientists, and stakeholders across Omaze. You embrace AI-assisted and agentic coding as part of how modern engineering gets done and apply it with the same judgement and rigour you bring to everything else 🔧 WHAT YOU’LL BE DOING - Build and maintain robust ingestion and processing pipelines from our product systems, third-party tools, and external partners, using modern, well-tested code that holds up as the business changes - Build the data quality, observability, and lineage frameworks that make decision-critical datasets trustworthy, and make problems easy to find and fix when they happen - Work with engineers across the organisation to ensure that events are properly constructed, with the right parameters and nomenclature, so that the data flowing into our platform is trustworthy by design, not by accident (potentially through data con
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