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Greater London, England, United Kingdom
Hybrid
As the Sr. Data Engineer - Architecture, you will drive data infrastructure that enables data-informed decision-making, applying modern engineering and distributed systems practices. You will partner closely with Product Managers, Data Analysts, Software Engineers, and business stakeholders to deliver stable, high-quality data pipelines, enterprise reporting, and datasets that support analytics and machine learning use cases.
A primary responsibility of this role is to optimize and maintain our OLTP databases, ensuring reliability, performance, and production readiness. This includes auditing systems, improving data quality, refactoring legacy pipelines, and applying best practices for schema design, indexing, and query performance. In parallel, you will build and maintain scalable data systems that power advanced analytics across the business.
You will design performance testing and release validation frameworks to prevent regressions and ensure data integrity, while establishing strong production processes such as monitoring, alerting, backup and recovery, access controls, and incident response.
5+ years of experience as a Database Engineer, Data Engineer, or similar role
Experience designing, implementing, and maintaining high performant, scalable OLTP systems.
Hands-on experience and advanced knowledge of SQL (e.g., Postgres, Snowflake)
Strong experience with data modeling, data warehouses, and lakehouse architectures
Experience designing and implementing scalable data architectures, including batch and streaming pipelines
Experience building ELT pipelines with dbt and Snowflake
Intermediate to advanced Python development skills
Experience assessing and improving existing database systems, including performance tuning (indexing, query optimization, partitioning) and data quality remediation
Strong understanding of database internals and transactional systems
Experience implementing backup, recovery, and high-availability strategies
Experience designing and implementing performance/load testing frameworks for data systems
Knowledge of benchmarking, regression testing, and release validation processes
Experience building automated testing pipelines to ensure data quality and system performance across deployments
Experience defining and maintaining production database processes, including monitoring, alerting, and incident response
Familiarity with observability tools and practices (logging, metrics, tracing)
Strong understanding of SLAs, SLOs, and data reliability best practices
Experience with AWS data technologies (Glue, Kinesis, Lambda)
Experience with orchestration tools (Airflow)
Experience with infrastructure-as-code (Terraform)
Knowledge of the Software Development Lifecycle
Experience with CI/CD pipelines, especially for data systems
Experience with containerization (Docker, Kubernetes)
Knowledge of encryption, anonymization, and tokenization
Experience with open table formats and data catalogs
Familiarity with data observability tools (e.g., Monte Carlo, Datadog, Prometheus)
Detail-oriented, with a strong data quality mindset
Strong problem-solving and troubleshooting skills with a proactive approach to system reliability
Self-starter with a bias toward ownership and continuous improvement
Comfortable bringing structure and best practices to ambiguous or legacy environments
Thrives in a fast-paced, startup-oriented, team-focused culture
Positive, collaborative, and energetic attitude
Excellent verbal and written communication skills
Ability to clearly explain complex technical issues to both technical and non-technical audiences