206K–290K USD / year
Data Engineer, Foundations
DataHybrid — San Francisco, California, United States
Published on 2026-11-13
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About this role
Build and operate large-scale data pipelines and foundational datasets for Superhuman’s integrated product suite. The role spans data architecture, ETL frameworks, data modeling, and production reliability, processing billions of events daily.
About the company
Superhuman is an email productivity tool designed to enhance users' efficiency and workflow. The company serves professionals looking to optimize their email management through innovative features and streamlined processes.
The team
Data Foundations team within the Data Platform organization.
Stack
SQLPythonSparkDatabricksDelta LakedbtSnowflakeBigQuery
What you'll do
- Design and own scalable data pipelines and data lakes processing billions of daily events.
- Enable secure, reliable batch and real-time data processing.
- Own data quality, freshness, monitoring, and reliability for foundational datasets.
- Build ETL frameworks and tools that support self-service data modeling.
- Partner across product, engineering, data science, and ML teams to deliver data products.
- Help shape the team’s charter and technical roadmap.
What we're looking for
- 7+ years building and operating large-scale distributed systems, with data engineering and modeling expertise.
- 5+ years operating live production environments and data-intensive workflows.
- Strong SQL and Python skills with hands-on Spark and lakehouse or cloud data warehouse experience.
- Experience with ETL/ELT patterns, orchestration, data quality, and Git-based CI/CD for data.
- Strong data modeling, warehouse design, and data observability skills.
- Ownership, strategic judgment, and clear cross-functional communication.
Nice to have
- Experience with high-throughput streaming systems such as Kafka, Flink, or Spark Structured Streaming.
- Experience with Delta Lake, Iceberg, Hudi, or Terraform.
- Track record of building self-service data products, tools, or frameworks.
- Experience partnering with analytics, data science, or ML teams.
Benefits
- Medical, dental, vision, mental health, and fertility benefits.
- 401(k) matching.
- Paid parental leave.
- 20 days of PTO, 12 paid holidays, two floating holidays, and flexible sick time.
- Stipends for caregiving, pet care, wellness, and home office expenses.
- Annual professional development budget.