dbt Cloud Solution Architect

Company Overview Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, skyscrapers, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our Product Lifecycle Management software.

Position Overview You will lead the architecture and production evolution of the dbt Cloud and Snowflake transformation layer that supports data products, analytics, and AI use cases for Global Customer Success. Your primary charter is to make the platform more economical and scalable as data volume, complexity, and consumption grow. You will combine expert ELT pipeline design with hands-on dbt optimization, operational job monitoring, and production release practices.

This is not a documentation-only architecture role. You will investigate expensive or fragile workloads, design reusable patterns, implement foundational solutions, and help engineers run their daily work in dbt effectively. You will also establish the delivery controls to fully orchestrate bronze/silver/gold layers into downstream data use.

Key Responsibilities You will make an impact by

• Own the technical architecture and engineering standards for dbt Cloud transformations on Snowflake, including project structure, model layering, dependencies, testing, documentation, orchestration, and deployment patterns.
• Reduce the marginal compute cost of roadmap growth by analyzing query and job behavior, identifying inefficient transformations, and prioritizing the optimizations with the highest return.
• Design efficient approaches for complex, high-volume datasets, including appropriate model boundaries, incremental processing, materialization strategies, workload sizing, scheduling, and reuse of transformed data.
• Establish production-grade DEV, QUA, and PRD dbt job processes with CI/CD controls, automated validation, release gates, regression detection, rollback approaches, and auditable release documentation.
• Define operational monitoring for dbt mart distribution of data products, working closely with data scientists, dashboard developers, and AI application owners to ensure outputs are meeting their needs.
• Coach dbt data engineers and collaborate with the wider data team through design reviews, code reviews, reusable examples, technical documentation, and knowledge transfer while remaining hands-on with complex implementations.

Qualifications

• Advanced, hands-on expertise with dbt Cloud in production environments. Deep dbt capability is a non-negotiable requirement for this role.
• Demonstrated experience designing, refactoring, and optimizing large dbt projects and complex SQL transformations on Snowflake or a comparable cloud data platform.
• Strong knowledge of dbt model design, tests, macros, packages, documentation, lineage, orchestration, incremental models, snapshots, and materializations.
• Experience measuring and improving cloud data workload efficiency using query profiles, execution history, warehouse behavior, job telemetry, and cost or consumption data.
• Experience designing CI/CD and release-management practices for analytics engineering, including environment separation, automated checks, controlled promotion, regression handling, and rollback.
• Ability to translate business and data-product requirements into maintainable technical architecture and to communicate tradeoffs clearly across a global team.

Preferred Skills

• dbt certification or substantial evidence of advanced dbt practice.
• Expertise with managing Snowflake performance and cost optimization.
• Experience running transformations and data products for analytics, customer health, telemetry, or AI workloads.
• Experience establishing engineering standards in a growing data organization and establishing best practices.
• Familiarity with APIs, data contracts, semantic layers, metadata/catalog tools, and enterprise data governance.

What Success Looks Like

• Measurable reductions in normalized compute consumption, avoidable job runtime, and repeated transformation work.
• A documented and adopted set of reusable dbt patterns for efficient delivery.
• A functioning, controlled path from development through test to production, including regression and rollback practices.
• Improved reliability, observability, documentation, and maintainability of production dbt workloads.
• Greater internal dbt capability that can support a continuous increase in incoming requests and use cases.

We are Siemens At Siemens Software, flexibility is how we work—hybrid by default, built on trust and autonomy. Together, 30,000 people across more than 200 countries build technology that shapes the real world. You'll grow through real projects, strong technical peers, and global mobility, backed by the scale and benefits of an industrial software leader. We're committed to equality and inclusion, and we hire based on merit, skills, and impact. Bring your curiosity and creativity and help us shape tomorrow!

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