Principal Data Engineer

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