Senior Data Engineer

Role overview

Join a growing data engineering team supporting a broadband infrastructure monitoring platform and a large federal broadband equity initiative. This is a hands-on senior role focused on designing, building, and operating the data infrastructure that powers multi-state programs at scale, with significant influence over architecture, tooling, and team practices.

Responsibilities

- Partner with product and engineering counterparts to translate requirements into scalable, cost-aware technical architectures and contribute to roadmap and build-versus-buy decisions - Design and maintain data lake and warehouse infrastructure, including partitioning, storage optimization, lifecycle management, and multi-tenant schemas across cloud providers - Build and operate production-grade orchestration pipelines for ingestion, transformation, and export workflows - Develop DBT models and data quality checks, and implement automated validation, cleansing, and reconciliation routines across complex multi-source flows - Write and optimize analytical and operational SQL across managed data warehouses, and build reusable Python utilities that raise engineering standards - Support AI-enabled features by integrating vector databases and LLM workflows into ELT pipelines, and help advance time-series and event-based data patterns - Mentor engineers through code review, pair programming, and architectural guidance, and help shape team standards for testing, documentation, and observability

Requirements

- Five or more years of data engineering experience owning production systems end-to-end - Strong proficiency in Python and SQL, with the ability to write production-quality code - Deep hands-on experience with AWS data services such as S3, Athena, RDS or PostgreSQL, ECS, Lambda, IAM, and CloudWatch - Production experience with Apache Airflow or comparable orchestration tools - Experience designing or operating large-scale time-series, event-based, or streaming data systems - Familiarity with vector databases and LLM integration patterns using frameworks like LangChain or AWS Bedrock - Track record of mentoring engineers, making pragmatic trade-off decisions, and communicating technical choices clearly to non-engineers

Nice to have

- Experience managing data for SaaS platforms - Experience with large-scale spatiotemporal data pipelines, PostGIS, spatial indexing, or tiling - Exposure to MLflow and deploying production ML pipelines

Benefits and work setup

- Fully remote work from anywhere with strong internet connectivity - Competitive salary band of roughly $190,000 to $240,000 plus meaningful equity - Growing benefits package with employee input into which benefits are prioritized - Optional in-person team retreats convened two to three times per year - Opportunity to help shape the roadmap for a program tied to significant federal broadband funding