Senior Data Analytics Engineer, Dotmatics

• We are looking for people who have 5+ years of experience in data analysis with a deep understanding of cloud operations, cloud architecture, or a related technical infrastructure role, with demonstrated experience analysing and optimising cloud spend at enterprise scale
• Advanced SQL and/or Python, particularly data science and report generation frameworks
• Hands-on experience with AWS or Databricks, with a strong preference for multi-cloud expertise
• Strong understanding of cloud-native architectures including containerisation, serverless, managed databases, and distributed systems
• Experience with observability and monitoring platforms such as Datadog, CloudWatch, or equivalent
• Ability to produce clear written and visual communication of technical findings for executive audiences
• Familiarity with version control systems (e.g., Git) and CI/CD workflows for data engineering projects
• BI tools (e.g., Looker, Mode, Tableau)
• Ability to work directly with business teams to align on KPIs, define requirements, and deliver impactful insights
• Comfortable writing and maintaining data dictionaries, dbt Docs, or internal wikis
• Ability to design and build BI assets that are explainable and sustainable whether they are excel spreadsheets, BI dashboards or powerpoint presentation
• Research shows us the confidence gap and imposter syndrome can get in the way of meeting outstanding candidates, so please don’t hesitate to apply — we’d love to hear from you

Desirable

• Dotmatics is seeking a Data Analytics Engineer to support the Luma team, you will own the operational health, architecture intelligence, and cost optimization of our cloud environments, AI usage customer data and more
• In this role you will develop a deep understanding of our infrastructure, connect architectural decisions to business outcomes, and give the engineering leadership, Senior leadership and Corporate Data teams the visibility they need to make smart, cost-conscious decisions at scale
• Cloud Data Architecture and Cost Intelligence:
• Develop and maintain a comprehensive technical understanding of the organization’s cloud architecture across all major infrastructure components
• Map the relationship between architectural decisions and cost drivers, producing clear models that connect system behaviour to spend
• Identify cost anomalies, inefficiencies, and optimization opportunities across compute, storage, networking, and managed services
• Build and maintain dashboards and reporting that give engineering and leadership real-time visibility into cloud cost trends
• Develop high-impact reporting and analytics providing visibility into engineering and customer operations, lead efforts to scale reporting through automation
• Partner with internal stakeholders (Sales, Product, Marketing, Finance, etc.) to increase the understanding, visibility and use of business metrics, encourage self-service analytics
• Perform ad hoc data exploration and analysis to answer immediate business needs
• Identify opportunities to improve data quality, performance, and observability
• Help develop standards for analytics engineering best practices
• Technical Operations:
• Help define and enforce cloud governance policies including tagging standards, resource lifecycle management, and rightsizing practices
• Partner with engineering teams to review architectural decisions for cost impact before and after deployment
• Maintain runbooks and documentation for operational data management processes
• Stakeholder Collaboration:
• Serve as the primary point of contact for cloud cost and operations questions from engineering, finance, and executive stakeholders
• Translate complex technical findings into clear, business-oriented recommendations for non-technical audiences
• Collaborate with engineering leadership on roadmap decisions that have infrastructure and cost implications
• Work cross-functionally on handoffs and alignment across shared operational responsibilities