Data Engineer, Foundations

Superhuman • San Francisco, CA

2d • $206,000 - $290,000 • Hybrid

About The Position

Superhuman is seeking a Data Engineer to join its Data Foundations team within the Data Platform organization. The team owns foundational datasets and data models that define core business entities and support data teams and data scientists across the company. Superhuman processes petabytes of data and billions of events daily, creating an opportunity to help shape the strategy and architecture of large-scale data systems and bring them into production.

The role focuses on designing, building, and operating robust, scalable, and reliable data pipelines and systems for product features and data-driven decisions. Work spans ETL pipelines, data lakes, performance-oriented processing, and ETL framework development. The position also partners with product teams, engineering, machine learning, and data science, while collaborating with leadership on the team’s charter and technical roadmap. Superhuman describes its working model for this role as hybrid, combining focus time with in-person collaboration.

Requirements

• At least 7 years of experience building and operating large-scale distributed systems, with expertise in data engineering and data modeling.
• At least 5 years of experience running live production environments, including high-load or data-intensive workflows, with a focus on uptime and reliability.
• High proficiency in SQL and Python, with deep hands-on experience in Spark and a modern lakehouse or cloud data warehouse such as Databricks, Delta Lake, dbt, or Snowflake.
• Strong knowledge of ETL/ELT design patterns, orchestration tools such as Airflow, dbt, or Dagster, data quality frameworks, and CI/CD for data with Git-based deployments.
• Strong data modeling and data warehouse design skills, and a rigorous approach to data quality and observability.
• Hands-on experience with modern data storage technologies such as Delta Lake, Snowflake, BigQuery, or Redshift.
• Ability to evaluate in-house development against third-party solutions while balancing speed, scalability, and long-term viability.
• Clear communication and collaboration skills across diverse teams and stakeholders, including the ability to translate business needs into robust data solutions and trustworthy metrics.
• Ownership mindset and ability to take end-to-end responsibility for systems, from strategy and architecture through production and ongoing reliability.
• Acumen for scalable, complex data processing; holistic problem-solving; and commitment to raising the team’s technical craft.
• Self-starting problem-solving skills, ability to think from first principles and manage priorities across multiple projects, and comfort in a fast-paced, results-driven environment.

Nice To Haves

• Experience with high-throughput, real-time streaming systems such as Kafka, Flink, or Spark Structured Streaming at a scale of billions of events per day.
• Familiarity with data lake and lakehouse technologies such as Delta Lake, Iceberg, or Hudi, and managing cloud infrastructure as code such as Terraform.
• Track record of building self-serve data products, tools, or frameworks relied on by other teams.
• Experience partnering with analytics, data science, or machine learning teams as a strategic data partner to productionize data and models.

Responsibilities

• Architect, build, and own large-scale data pipelines and data lakes using Spark and Databricks to process billions of daily events into reliable datasets.
• Design and implement platform solutions that keep data available, secure, and scalable for real-time and batch processing.
• Own data quality, freshness, and reliability for foundational datasets by implementing automated checks, monitoring, and alerting.
• Build ETL frameworks and tooling that enable data teams and data scientists to self-serve and create clean, documented, reusable datasets for core business entities.
• Partner with product teams, back-end engineers, ML engineers, and Data Science to turn business questions into data products for business-critical features, research, and experimentation.
• Collaborate with leadership to shape the team’s charter and technical roadmap.

Benefits

• Competitive pay.
• Medical, dental, vision, mental health, and fertility benefits.
• Disability and life insurance options.
• 401(k) and RRSP matching.
• Paid parental leave.
• 20 days of paid time off per year, 12 paid holidays per year, two floating holidays per year, and flexible sick time.
• Stipends for caregiving, pet care, wellness, home office, and more.
• Annual professional development budget and opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed