Principal Analytics Engineer

The Principal Analytics Engineer - Data Architecture is a principal individual contributor and technical leader on the Customer Analytics team. This role builds and operates CMT's relational data marts, dbt data products, and reusable semantic layer for BI, data science, and AI.

The position partners with Customer Solutions, Data Engineering, Product, and Customer Success to resolve ambiguous needs, align definitions and governance, and deliver scalable, well-documented data products with clear ownership and customer impact.

Responsibilities:

• Use independent judgment and discretion to architect relational data marts and high-scale dbt models in Databricks and Delta Lake, using dimensional modeling and software engineering practices for scale, maintainability, and cost efficiency

• Own data products end to end, from discovery and contracts through automated testing, CI/CD deployment, documentation, monitoring, support, and controlled change

• Design and productionize a modular semantic layer and shared data services that deliver governed metrics, dimensions, metadata, approved access paths, and vector-enabled retrieval across BI, data science, and AI

• Operate high-availability data and reporting products to agreed SLAs through freshness and quality alerts, lineage, observability, proactive issue detection, and urgent resolution

• Align metric definitions, governance, and domain ownership; resolve ambiguity with internal and external stakeholders, and raise team standards through reusable dbt frameworks, technical reviews, mentoring, and AI-assisted development

• Complete any additional tasks as they arise

Qualifications:

• Bachelor’s degree or equivalent years of experience and/or certification in a related field

• 7+ years of experience in analytics engineering, data engineering, or data architecture with a bachelor's degree, or 5+ years with a graduate degree

• Expert SQL and dimensional modeling, including grain, star and snowflake schemas, conformed facts and dimensions, SCD Type 2, snapshots, event models, and incremental processing

• Extensive dbt experience with modular models, macros, packages, custom tests, documentation, and source freshness; hands-on Databricks, Delta Lake, and Spark experience

• Production engineering experience with Git, CI/CD, automated testing, observability, SLA support, performance tuning, data catalogs, lineage, and policy-aware access

• Experience with semantic layers, embeddings, semantic search, and vector databases, plus exposure to AI/LLM tools as data consumers; proven product-oriented ownership and cross-functional influence

Compensation and Benefits: