Principal Software Engineer – Data Platform

May 23, 2026
Application ends: August 22, 2026
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Job Description

What Youll Do :

– Semantic Model Architecture : Design and evolve the semantic modeling layer that serves as the single source of truth for metrics, dimensions, entities, and business logic across all data products.

– Define the standards for how semantic models are authored, versioned, tested, and governed.

– Evaluate and drive the semantic layer technology strategy (e.g., dbt MetricFlow or equivalent).

– Data Product Enablement : Architect how the semantic layer is consumed across distinct product surfaces such as Reporting (high-performance BI platform for customers), and Agentic Analytics (metadata-rich, discoverable interfaces that enable AI agents to reason over and query the semantic layer).

– Partner with adjacent teams to ensure the semantic layer meets each products unique requirements.

– Performance & Scale : Own query performance, materialization strategies, pre-aggregation patterns, and cost optimization.

– Ensure the semantic layer is highly performant and scalable as data volumes and consumer demand grow.

– Platform & Governance : Build the semantic layer as a true platform experience : self-service metric onboarding, developer-friendly abstractions, clear documentation, data validation, and governance guardrails.

– Make it easy for other teams to extend the semantic layer without compromising consistency or quality.

– Technical Leadership : Operate as a technical leader across the Data & Reporting Platform organization.

– Participate in and drive design sessions across teams.

– Mentor engineers, manage stakeholder and leadership alignment.

– Contribute to architecture decisions that span from data foundations through reporting and analytics.

– Champion high-quality code with corresponding test coverage.

– AI-Augmented Engineering : Use AI coding tools (Claude, Cursor, Copilot) as a core part of your daily workflow.

– Drive adoption patterns, build team-specific contexts and workflows, and set the standard for how the team multiplies velocity through AI-assisted development.

What Youll Bring :

– 10+ years of experience in Software Engineering or Data Engineering roles, including experience with large-scale, high-traffic, fault-tolerant systems.

– Deep experience with semantic modeling, data engineering, data lakehouse, and data product development.

– Track record of building platform-level abstractions consumed by multiple product teams.

– Strong experience with the DBT ecosystem.

– Experience with semantic layer technologies (e.g., dbt MetricFlow or similar) is highly preferred.

– Expert-level SQL and Python skills.

– Experience with query optimization, materialization strategies, and performance tuning at scale.

– Experience with modern data platform technologies : Snowflake, ClickHouse, or similar OLAP/columnar engines.

– Familiarity with Spark and streaming platforms (Kafka, Kinesis).

– Experience designing APIs and interfaces for domain specific data products.

– Demonstrated proficiency with AI coding tools (eg Claude, Cursor) as part of your regular engineering workflow; not just familiarity, but active daily use.

– Experience leading the architecture and design of systems (architecture, design patterns, reliability, and scaling).

– Strong communication and technical writing skills.

– Ability to empathize with users and champion for their experience.

– M.S. or PhD in Computer Science or a related field.

Highly preferred :

– Experience building semantic layers that serve both human analysts and programmatic/AI consumers.

– Experience with data governance frameworks, metric versioning, or data product catalogs.

– Familiarity with LLM-friendly data interfaces; designing schemas and metadata that enable AI agents to discover and query data effectively.

– Experience with data validation and quality frameworks (e.g., Monte Carlo, Great Expectations).

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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