Principal Data Engineer

Utilidata•Ann Arbor, MI

1d•Remote

About The Position

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.

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

Nice To Haves

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

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