Principal Data Engineer

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. The Principal Data Engineer will own the technical direction and execution of the data engineering platform, making critical design decisions and guiding the team to deliver reliable data infrastructure.

Responsibilities:

• Architect and contribute directly to core platform components, including ingestion pipelines, transformation frameworks, data models, and orchestration
• Define and evolve the multi-quarter technical roadmap for the data platform, balancing new capabilities, reliability investments, and technical debt reduction in alignment with the broader platform architecture
• Drive evaluation and adoption of tooling across the stack, ensuring choices are well-reasoned and aligned with where the platform needs to go
• Lead architecture reviews and design discussions, ensuring decisions are well-reasoned, documented, and understood by the team
• Cut through ambiguity by asking the right questions early about data quality, schema evolution, and downstream dependencies, and identify risks before they become crises
• Translate complex data infrastructure decisions for non-technical stakeholders without oversimplifying, and break vague product requirements into clear engineering tasks and acceptance criteria
• Partner closely with data science leads and cross-functional teams to surface dependencies and constraints early and prioritize improvements that unlock productivity
• Run a lightweight but effective backlog and planning process, keeping the team focused and unblocked
• Mentor and grow engineers with an emphasis on raising technical depth — delegate meaningful work, pair on hard problems, and create opportunities for others to stretch
• Set code review standards, testing philosophy, and engineering best practices that make the whole team better, including data validation, pipeline testing, and schema management
• Ensure data systems work reliably in production — instrumented, observable, and operable, with clear SLAs on freshness, completeness, and accuracy

Requirements:

• At least 8 years of experience in data engineering, with 2+ years operating at a principal or staff level
• Proven ability to design and evaluate end-to-end data platforms across ingestion, transformation, storage, and serving, with clean contracts between layers
• Deep understanding of data pipeline design, with fluency in the patterns and tradeoffs of batch and streaming pipelines at scale
• Strong understanding of data modeling and storage strategies
• Strong software engineering fundamentals, with the depth to evaluate code quality and set architectural standards
• Strong experience with cloud data infrastructure (AWS, GCP, or Azure) and the surrounding ecosystem
• Demonstrated ability to lead technical teams, set direction, and grow engineers without relying on formal authority
• Experience with streaming architectures (Spark Structured Streaming, Delta Live Tables, Kafka)
• Familiarity with data quality and observability tooling (Great Expectations, Monte Carlo, Soda, or similar)
• Background working with visualization tools connected to Databricks (Databricks Dashboards, Tableau, Sigma, Power BI)
• Experience with data collection from edge devices
• Experience supporting ML workflows, including feature engineering pipelines, feature stores, or model input data preparation