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Senior Data Engineer – Data Platform and Semantic Architecture and Engineering

LinkedIn Cambia Health Solutions Portland, OR
Not Applicable Posted March 30, 2026 3 variants Job link
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Requirements
  • Experience designing and implementing semantic layers is critical.
  • If you have hands-on experience with tools such as Snowflake semantic views, AtScale semantics, dbt semantics, Microstrategy Archtect / Semantic Graph, or Business Objects Universes, and thrive on variety and challenge in your work, we encourage you to apply.
  • If you’re a motivated and experienced Senior Data Engineer with a consultant’s mindset and a passion for building semantic layers and analytics platforms, we want to hear from you.
  • Experienced technical leader with deep expertise in dimensional modeling, semantic layer design, and analytics enablement, a strong background in building and optimizing data pipelines, excellent SQL skills, and hands‑on expertise with Snowflake, dbt or other ETL tools, and modern ETL/ELT practices.
  • Experience developing, implementing, refining, and troubleshooting semantic layers.
  • Hands‑on experience with at least one enterprise semantic technology (e.g., Snowflake semantic views, AtScale, dbt semantics, MicroStrategy, Business Objects).
  • 8 years relevant experience in a multi-platform environment, including, but not limited to application development or database development
  • At least 2 years working with Snowflake or similar cloud data platforms
  • Senior or principal-level experience in Data Engineering or Data Platform Engineering.
  • Deep expertise in dimensional modeling, semantic layer design, and analytics enablement.
  • Advanced proficiency with Snowflake, dbt, and SQL, including performance tuning at scale.
  • Hands‑on experience with at least one enterprise semantic technology (e.g., Snowflake semantic views, AtScale semantics, dbt semantics, MicroStrategy Architect / Semantic Graph, Business Objects Universes.
  • Proven ability to lead through influence across teams and disciplines.
  • Strong mentoring and coaching skills, particularly with senior engineers.
  • Demonstrated success driving standards, patterns, and long‑term platform strategy.
  • Collaborate with the Data Product and Data Modeling teams to establish patterns for metric definitions, conformed dimensions, and reusable semantic assets.
  • Partner with Enterprise and Domain Architects to ensure semantic consistency, interoperability, and scalability
  • Ensure semantic layers are optimized for BI tools, self‑service analytics, and agentic/AI consumption.
  • Agentic Intelligence Enablement
  • Enable trusted data foundations for agent‑based analytics and AI assistants, ensuring semantic models are consumable by LLMs and analytic agents.
  • Quality, Governance & Compliance
  • Champion data governance, security, and compliance across operational, analytical, semantic, and agentic platforms.
Preferred Skills
  • Preferred Key Experience:
  • Experience developing, implementing, refining, and troubleshooting semantic layers.
  • Hands‑on experience with at least one enterprise semantic technology (e.g., Snowflake semantic views, AtScale, dbt semantics, MicroStrategy, Business Objects).
  • We strongly prefer candidates with a broad range of experience—such as that gained through consulting roles or cross-functional project work—who can bring fresh perspectives and innovative solutions to our data ecosystem.
  • Consulting or cross‑industry background bringing diverse perspectives to complex data ecosystems.
  • Experience supporting AI‑enabled analytics, agentic workflows, or metadata‑driven systems.
  • Python development for data engineering, automation, or platform tooling.
  • Experience integrating semantic layers with BI tools such as Tableau, Power BI, or Looker.
  • Enterprise Technical Leadership
  • Serve as a design authority for complex operational, analytic, data pipeline, and semantic architectures, reviewing and approving high‑impact solutions
  • Guide engineers on technical solutions and techniques to optimize compute and storage cost.
  • Data Platform & Pipeline Excellence
  • Oversee design and optimization of scalable ELT/ETL pipelines and data products, with a focus on reliability, performance, and cost efficiency.
  • Drive platform observability, proactive data quality monitoring, and automated validation frameworks.
  • Lead performance tuning efforts for Snowflake tables, views, materialized views, and semantic access patterns.
  • Semantic & Dimensional Model Engineering
  • Guide the design and implementation of enterprise semantic models on Snowflake, including facts, dimensions, metrics, and governed business logic.
  • Collaborate with the Data Product and Data Modeling teams to establish patterns for metric definitions, conformed dimensions, and reusable semantic assets.
  • Partner with Enterprise and Domain Architects to ensure semantic consistency, interoperability, and scalability
  • Ensure semantic layers are optimized for BI tools, self‑service analytics, and agentic/AI consumption.
  • Agentic Intelligence Enablement
  • Enable trusted data foundations for agent‑based analytics and AI assistants, ensuring semantic models are consumable by LLMs and analytic agents.
  • Collaborate with platform and AI teams to define contracts, metadata, and observability required for intelligent agents.
  • Influence responsible AI usage by embedding governance, lineage, and explainability into semantic designs.
  • Quality, Governance & Compliance
Education
  • (Not required) – Bachelor’s degree in computer science, Mathematics, Business Administration, Engineering, or a related field
  • (Not required) – Equivalent combination of education and experience