Senior Data Engineer

We are looking for a Data Engineer to own the reliability and performance of our data platform. You’ll collaborate with engineering and business teams to ensure a reliable, scalable data environment. Responsibilities Manage and optimise the Snowflake data warehouse, including query performance, warehouse sizing, resource utilisation, and cost efficiency. Maintain the reliability and performance of dbt projects, including data models, automated testing, documentation, and deployment processes. Oversee ELT pipelines, ensuring data availability, freshness, and compliance with established SLAs. Implement monitoring, alerting, and incident management processes, including root cause analysis and preventive measures. Establish data quality standards, automated validation, and observability practices across the data platform. Manage access controls, data governance policies, and compliance requirements. Monitor Snowflake consumption, implement cost optimisation strategies, and support platform budget planning. Develop operational runbooks, CI/CD workflows, and best practices for data engineering teams. Collaborate with Data Engineering, Analytics, BI, Security, and Finance teams to improve platform reliability and operational efficiency. Requirements Extensive experience in Data Engineering, Analytics Engineering, DataOps, or Data Platform Operations. Strong hands-on experience with Snowflake administration, performance tuning, warehouse management, and SQL optimisation. Advanced proficiency in dbt Core or dbt Cloud, including incremental models, macros, testing, and deployment. Proven experience managing production ELT pipelines using Fivetran, Airbyte, Stitch, or similar solutions. Solid understanding of DataOps principles, CI/CD, version control, automated testing, and deployment pipelines. Experience implementing data quality frameworks, monitoring, alerting, and incident response processes. Understanding of data governance, access management, and data platform reliability. Nice to Have Experience with data observability tools such as Monte Carlo, Elementary, or Datafold. Familiarity with workflow orchestration platforms such as Airflow, Dagster, or Prefect. Experience optimising cloud data platform costs, including Snowflake credit consumption and resource allocation. Knowledge of regulatory and compliance frameworks, including GDPR and SOC 2.