Principal Software Engineer, Data Platform

ServiceTitan

About The Position

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 three critical data products: Data Sharing, 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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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 three distinct product surfaces: Data Sharing (governed datasets for customers), 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.

Benefits

Flextime, recognition, and support for autonomous work: Flexible time off with ample learning and development opportunities to continue growing your career. We offer a comprehensive onboarding program, leadership training for Titans at all levels, and other programs and events. Great work is rewarded through Bonusly, peer-nominated awards, and more.
Holistic health and wellness benefits: Company-paid medical, dental, and vision (with 100% employer paid options and 90% coverage for dependents), FSA and HSA, 401k match, and telehealth options including memberships to One Medical.
Support for Titans at all stages of life: Parental leave and support, up to $20k in fertility services (i.e. IUI and IVF), surrogacy, and adoption reimbursement, on demand maternity support through Maven Maternity, free breast milk shipping through Maven Milk, pet insurance, legal advisory services, financial planning tools, and more.

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