Senior Staff Data Engineer

About the role

The Enterprise Data & Analytics (EDA) team is seeking a Senior Staff Engineer to lead the architecture and engineering of an AI‑ready enterprise semantic data platform. The platform will provide trusted foundational data models across Customer, Finance, GTM, and Product, a governed semantic layer for metrics, dimensions, entities, relationships, and business context, and conversational analytics over enterprise data. The role sits at the intersection of data engineering, data architecture, software engineering, and AI, partnering with architects, data engineers, data scientists, analyst teams, and AI teams to deliver reliable, observable, secure, and governed platform capabilities.

Responsibilities

• Define the enterprise semantic architecture for metrics, dimensions, entities, relationships, grain, time semantics, and business context over trusted foundational data models.
• Establish reusable semantic contracts so analytics, applications, and AI systems operate from consistent business definitions and governed data products.
• Evaluate and recommend the long‑term semantic platform architecture, including Snowflake Semantic Views, dbt Semantic Layer / MetricFlow, Cube, and other approaches.
• Design and build metadata‑driven platform capabilities for semantic discovery, deterministic metric computation, query generation, governed access, versioning, and extensibility.
• Define how conversational analytics systems consume enterprise semantics and structured data, partnering with AI teams on building Data Agents and other AI interfaces.
• Establish golden datasets and evaluation practices for semantic interpretation, generated‑query accuracy, metric correctness, and analytical answer quality.
• Lead complex, multi‑team initiatives from architecture and proof of concept through production adoption, operational support, and continuous improvement.
• Influence build‑versus‑buy decisions, mentor engineers, and raise the technical bar across Data Engineering, Analytics, BI, and AI.
• Treat the platform as an internal product by improving contributor experience, documentation, onboarding, adoption, and migration from duplicate implementations.

Basic Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related field, or equivalent practical experience.
• 10+ years of experience in data engineering, data architecture, distributed systems, software engineering, or related platform disciplines, with significant technical leadership experience.
• At least 3+ years of hands‑on experience in Semantic Layer implementation.
• Demonstrated experience designing and building large‑scale data, analytics, semantic, developer, or platform systems used by multiple teams (e.g., AtScale, Cube.dev, DBT Metric Flow) in production environments.
• Deep expertise with modern analytical warehouses and transformation frameworks, including Snowflake or Databricks, dbt, and cloud‑based ELT pipelines.
• Strong software engineering fundamentals and production experience with SQL and at least one programming language such as Python, Java, Go, Scala, or similar.
• Deep understanding of dimensional, entity‑based, and analytical data modeling, including grain, relationships, time dimensions, and metric definitions.
• Excellent communication skills and the ability to collaborate with executives, architects, engineers, analysts, data scientists, and business stakeholders.
• Proven ability to mentor senior engineers and influence technical decisions across multiple teams.

Preferred Experience

• Experience with one or more of Snowflake Semantic Views, dbt Semantic Layer / MetricFlow, Cube, LookML / Looker, or comparable semantic and metrics platforms.
• Experience with text‑to‑SQL, agent evaluation, MCP, or conversational analytics.
• Experience defining deterministic boundaries between governed metric computation and probabilistic AI reasoning.
• Experience evaluating vendors or open‑source platforms and translating architectural recommendations into an adoption roadmap.

Technology Stack

• ELT: MySQL CDC, Kafka, Snowflake, Fivetran, dbt Core & dbt Cloud, Astronomer, Alation, Montecarlo
• BI: Tableau, Looker
• Infrastructure: AWS, Kubernetes, Terraform, GitHub Actions

Compensation

The US annualized base salary range for this position is $200,000 – $300,000. The role may also be eligible for bonus, benefits, or related incentives. The final offer will be based on job‑related capabilities, applicable experience, and factors such as work location.