Data Engineering Principal

Better Trucks is building next-generation last-mile logistics technology that powers the delivery lifecycle from routing and driver assignment to real-time operations, customer visibility, and delivery performance. The Data Engineering Principal will own the architecture and execution of the core data platform, building trusted data models, pipelines, ingestion systems, orchestration, internal tools, and data quality processes while partnering with Analytics Engineering, BI, Finance, and Operations.

Responsibilities:

• Design and build standardized data models that normalize complex, customer- and source-specific data (MongoDB, Postgres, Firestore, carrier and customer systems) into trusted schemas for pricing, finance, and operations
• Own core BigQuery and Dataform pipelines, including schema design, incremental modeling patterns, testing, and documentation
• Build and operate ingestion into the warehouse via Datastream, Cloud Functions, and custom connectors for systems without clean CDC paths
• Set technical direction for orchestration (Composer/Airflow), including job design, dependency management, and cost/performance tradeoffs
• Drive data integrity through anomaly detection, quality monitoring, and root-causing discrepancies before they erode trust in reporting
• Build internal tools (Python, FastAPI/Flask, occasionally React) that automate operational workflows such as invoicing, ops data feeds, and reporting utilities
• Apply AI and LLM-based tooling pragmatically to automate repetitive internal workflows and accelerate data engineering work, without introducing fragile or unmaintainable systems
• Partner with Analytics Engineering and BI to ensure core data models serve reporting and forecasting without duplicative downstream logic
• Review designs and code from other data engineers, and raise the technical bar without adding unnecessary process
• Make build-vs-buy and architecture decisions with profitability, maintainability, and team adoption in mind

Requirements:

• • 8+ years of data engineering experience, including 2-3+ years operating at a staff/principal level of scope (org-wide architecture decisions, not just pipeline delivery)
• • Deep hands-on expertise in BigQuery or an equivalent cloud data warehouse, and SQL-based transformation tooling such as Dataform or dbt
• • Strong Python skills for pipeline and tooling work, with experience building and maintaining production services (FastAPI, Flask, or equivalent)
• • Demonstrated experience designing standardized or normalized data models from heterogeneous, complex source systems
• • Practical experience with workflow orchestration (Airflow, Composer, or equivalent) at production scale
• • Track record of making data trustworthy, including testing, monitoring, anomaly detection, and incident response for data issues
• • Strong analytical and communication skills, with the ability to explain technical tradeoffs to non-technical stakeholders in Finance and Operations
• • Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience
• • Experience in last mile delivery, parcel logistics, transportation management, or another operationally intensive business where data quality directly affects P&L
• • Experience with the GCP ecosystem, including Cloud Functions, Datastream, and Firestore
• • Familiarity with BI tooling such as Looker, and with BI migration or governance efforts
• • Hands-on experience with LLM-based tooling (e.g. Claude, GPT) for code generation, data pipeline automation, or internal agent/workflow tooling
• • Experience in a high growth technology company