Staff Data Engineer

Responsibilities:

• Set the technical direction for Lytx's cloud data warehouse and lakehouse platform, balancing performance, cost, and reliability at scale.
• Design data models, pipelines, and storage layers that support analytics, reporting, and machine learning across the business.
• Partner with data engineering, analytics, product, and AI teams to translate business needs into platform capabilities.
• Lead complex, cross-team technical initiatives from design through delivery, and represent the data platform in architecture reviews.
• Improve data quality, governance, and observability so teams can trust the data they build on.
• Raise the bar on engineering practices through code review, design documents, and mentorship of engineers at all levels.
• Evaluate and introduce new tools and approaches where they solve a real problem, not for their own sake.
• Support the platform's ability to scale with Lytx's data volumes, including new data from AI safety features.

Requirements:

• 10+ years of experience in data engineering, with a track record of designing and operating large-scale data warehouse or lakehouse platforms in production.
• Deep hands-on experience with a modern cloud data warehouse (e. g., Snowflake, BigQuery, or Redshift) and distributed data processing (e. g., Spark).
• Strong SQL and Python skills and experience building and maintaining data transformation pipelines (e. g., dbt, Airflow, or similar orchestration tools).
• Solid understanding of data modelling, warehouse design patterns, and the tradeoffs between batch and streaming architectures.
• Experience operating data infrastructure on a major cloud provider (AWS, Azure, or GCP).
• A history of leading technical projects across teams and influencing architecture decisions without direct authority.
• Clear written and verbal communication: you can explain a complex data model to both an engineer and a business stakeholder.
• A bias toward pragmatic solutions: you know when to build for scale and when to keep things simple.

Nice to Have:

• Experience with streaming data platforms (e. g., Kafka, Kinesis, or Flink).
• Experience supporting machine learning or AI feature pipelines.
• Background in IoT, telematics, video, or another high-volume sensor data domain.
• Experience with data governance, lineage, or cataloguing tools.