Senior Data Engineer
Who we are
About this role
What you’ll do
- Design, build, test and operate product-specific data pipelines, transformations, data models and serving interfaces using approved platform capabilities.
- Develop maintainable Python, SQL and dbt or equivalent code using automated testing, version control, continuous delivery and clear documentation.
- Implement agreed data contracts, semantic definitions, data-quality rules, metadata and lineage, alongside appropriate privacy and access controls.
- Own production readiness for assigned data-product components, including monitoring, runbooks, incident response, resilience, performance and cost.
- Work across batch, streaming, event-driven and API integration patterns to meet data-product requirements.
- Collaborate with Data Science and ML & AI Engineering on trusted feature, training, inference and outcome data.
- Diagnose and resolve complex production and data defects, helping reduce recurrence and improve operational reliability.
- Reuse approved platform capabilities and shared services rather than creating duplicate product-level solutions.
- Coach the Junior Data Engineer, supporting effective engineering practices and technical development without formal people-management accountability.
- Apply AI-assisted engineering tools with appropriate human review, security controls, privacy, traceability and production discipline.
- Collaborate across Product, Architecture and Platform teams to ensure solutions remain aligned with agreed technical standards.
Who you are
- Substantial practical experience in Data Engineering or software engineering for production data products.
- Strong Python and SQL skills and practical experience across data modelling, transformation and orchestration.
- Experience with dbt or equivalent transformation tooling.
- Production experience with Google Cloud Platform data services or equivalent cloud technologies.
- Experience implementing batch, streaming, event-driven and API integration patterns.
- Strong engineering practices across automated testing, continuous delivery, monitoring, data quality and operational support.
- Good knowledge of data contracts, semantic modelling, lineage, privacy, security and regulated-data handling.
- Experience taking responsibility for the reliability, resilience, performance, cost and supportability of production data components.
- Practical experience using AI-assisted engineering tools with appropriate review and security controls.
- The ability to collaborate effectively with Product, Architecture, Platform, Data Science and ML & AI Engineering.
- The ability to coach less-experienced engineers through technical guidance, problem-solving and knowledge sharing.
Not a perfect fit?
What's in it for you
- The opportunity to build and operate production-grade data products supporting customer, commercial and operational outcomes.
- Hands-on engineering experience across Python, SQL, data modelling, transformation, orchestration and cloud data services.
- Exposure to batch, streaming, event-driven and API-based integration patterns within a cloud-native engineering environment.
- Ownership across the production lifecycle, including monitoring, operational readiness, incident response, resilience, performance and cost.
- Close collaboration with Data Science and ML & AI Engineering, supporting the data required for model training, inference and outcome measurement.
- Opportunities to develop technical leadership skills by coaching a Junior Data Engineer while remaining focused on engineering delivery.
- Practical experience applying AI-assisted engineering within appropriate security, privacy, review and governance controls.
What skills you will learn
- Production Data Engineering, covering the design, delivery and operation of product-specific pipelines, transformations, models and services.
- Cloud-native Data Engineering, using Google Cloud Platform data services or equivalent technologies.
- Batch, streaming and event-driven engineering, alongside API-based integration patterns.
- Data-product operational excellence, including observability, monitoring, incident response, resilience, performance and cost management.
- Data governance and controls, including contracts, semantics, quality, metadata, lineage, privacy and security.
- Data Science and ML/AI integration, supporting trusted feature, training, inference and outcome data.
- Technical coaching, including helping less-experienced engineers develop effective engineering practices.
- AI-assisted engineering practices, with a focus on appropriate human review, security, traceability and production quality.
VOIS Equal Opportunity Employer Commitment
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