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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