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. For over a decade, we have applied AI to the electric grid - bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.
We're looking for a Principal Data Engineer to own the technical direction and execution of our data engineering platform. This role is responsible for setting architectural direction for the data systems that underpin our products, make critical design decisions about how we collect, process, store, and serve data at scale, and raise the bar for the entire team through your judgment, communication, and hands-on engineering. You'll operate at the intersection of deep technical work and cross-functional leadership, translating product goals into sound engineering plans and guiding the team through ambiguity to deliver real results. You'll own the component-level architecture for the data platform while working in close partnership with the platform architect to ensure alignment with the end-to-end platform vision and architecture. You'll join a diverse team of experts who are mission-driven, collaborative, and adaptive, and guide the team through the challenges of building reliable, performant data infrastructure in a fast-moving environment.
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
Minimum Qualifications
- 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
Enhanced Qualifications (Nice to Have)
- 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
Salary Range: $170,000 to $210,000 base compensation depending on experience plus stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands.
Location: This position can be performed remotely from anywhere in the United States.
Our Commitments
Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws.
We are committed to:
- Creating a diverse and inclusive workplace that is welcoming, supportive, affirming, and respectful
- Empowering employees to solve problems and work together to make a difference
- Providing mentorship and growth opportunities as part of a collaborative team
- A flexible work environment with flexible paid time off
- Competitive compensation and benefits, including health, dental, vision, and employer-match 401k
