Asks for

The ad says“Experience designing APIs and interfaces for domain specific data products.”
The ad says“Experience leading the architecture and design of systems (architecture, design patterns, reliability, and scaling).”
The ad says“Demonstrated proficiency with AI coding tools (eg Claude, Cursor) as part of your regular engineering workflow; not just familiarity, but active daily use.”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Demonstrated proficiency with AI coding tools (eg Claude, Cursor) as part of your regular engineering workflow; not just familiarity, but active daily use.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Strong experience with the DBT ecosystem. Experience with semantic layer technologies (e.g., dbt MetricFlow or similar) is highly preferred.”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Expert-level SQL and Python skills. Experience with query optimization, materialization strategies, and performance tuning at scale.”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Expert-level SQL and Python skills. Experience with query optimization, materialization strategies, and performance tuning at scale.”
The ad says“Expert-level SQL and Python skills. Experience with query optimization, materialization strategies, and performance tuning at scale.”
The ad says“Expert-level SQL and Python skills. Experience with query optimization, materialization strategies, and performance tuning at scale.”
The ad says“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.”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Experience with modern data platform technologies: Snowflake, ClickHouse, or similar OLAP/columnar engines. Familiarity with Spark and streaming platforms (Kafka, Kinesis).”
The ad says“Expert-level SQL and Python skills. Experience with query optimization, materialization strategies, and performance tuning at scale.”
The ad says“Strong communication and technical writing skills. Ability to empathize with users and champion for their experience.”

Ready to be a Titan?

The Data & Reporting Platform team powers ServiceTitan’s growth by delivering high-quality, low-latency, and reliable data and BI products that enable trust, acceleration, and data-driven decision-making for our customers and across ServiceTitan.

We are looking for a Principal Engineer to own the semantic model architecture at the heart of our data platform. The semantic layer is the single source of truth for business metrics and logic, and it powers critical data products such as Reporting, and Agentic Analytics. This role sits at the intersection of data modeling, platform engineering, and product thinking — you’ll define how data is modeled, governed, and consumed at scale across multiple product surfaces.

This is a T-shaped role: deep expertise in semantic modeling and data architecture, with the breadth to operate across the full data platform stack at the principal level. You’ll partner closely with our Data Foundations team (which owns ingestion and storage), our Reporting team (which owns the reporting experience), and teams building agentic AI capabilities; ensuring the semantic layer is the performant, scalable, and extensible foundation they all depend on.

What You’ll 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 product’s 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 You’ll 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.
B.S., 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).

To effectively support our international teams, this position requires flexibility to overlap with US working hours as needed.

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Be Human With Us:

Being human isn’t about checking every box on a list. It’s about the experiences we have, people we meet, and the perspectives we share. So, if you have the skills but are hesitant to apply because of your background, apply anyway. We need amazing people like you to help us challenge the conventional and think differently about the problems that we’re solving. We’re in this together. Come be human, with us.

Use of AI Technology:

We use technology, including automated and AI-assisted tools, to support certain aspects of our recruitment process. These tools are designed to improve efficiency and enhance the candidate experience. AI tools are not used to make hiring decisions; all hiring decisions are made by our hiring teams.

At ServiceTitan, we celebrate individuality and uniqueness. We believe that the convergence of fresh perspectives and experiences from all walks of life is what makes our product and culture so great. We do not discriminate against employees based on race, color, religion, sex, national origin, gender identity or expression, age, disability, sexual orientation, or any other characteristic protected by applicable laws.

Asks for

Values