Data Engineer, Foundations
About The Position
Superhuman is seeking a Data Engineer to join their Data Foundations team. This role is crucial for building and maintaining a world-class data platform that handles over 70 billion daily events. The Data Foundations team is part of the Data Platform organization and is responsible for the foundational datasets and data models that define the core entities of Superhuman's business. These serve as building blocks for other data teams and data scientists within the company. The team develops and operates large-scale ETL pipelines that process petabytes of data and billions of events daily. This is a high-impact role at the intersection of data engineering, data-intensive applications, and data modeling, where the systems built directly influence the efficiency of other Data teams.
Requirements
- 3+ years of experience running live production environments, including high-load or data-intensive workflows, with a focus on uptime and reliability.
- Proficient in SQL and Python, with deep hands-on experience in Spark and a modern lakehouse or cloud data warehouse (Databricks, Delta Lake, dbt, Snowflake, or similar).
- Strong knowledge of ETL/ELT design patterns, orchestration tools (e.g., Airflow, dbt, or Dagster), data quality frameworks, and CI/CD for data with Git-based deployments.
- Data modeling and data warehouse design skills, along with a rigorous approach to data quality and observability.
- Hands-on experience with modern data storage technologies (for example, Delta Lake, Snowflake, BigQuery, or Redshift).
- Communicate clearly and collaborate well across diverse teams and stakeholders, turning business needs into robust data solutions and trustworthy metrics.
- Strong ownership mindset, taking end-to-end responsibility for the systems you build, from strategy and architecture through production and ongoing reliability.
- Strong acumen for scalable, highly complex data processing, think holistically about problems, and raise the bar on your team’s craft.
- Self-starting problem-solver who thinks from first principles, manages priorities across multiple projects, and thrives in a fast-paced, results-driven environment.
Nice To Haves
- Experience operating large-scale distributed systems, as well as in data engineering and data modeling.
- Experience with high-throughput, real-time streaming systems (for example, Kafka, Flink, or Spark Structured Streaming) at the scale of billions of events per day.
- Comfort with data lake and lakehouse technologies (for example, Delta Lake, Iceberg, or Hudi) and managing cloud infrastructure as code (for example, Terraform).
- A track record of building self-serve data products, tools, or frameworks that other teams rely on.
- Experience partnering with analytics, data science, or machine learning teams as a strategic data partner to productionize data and models.
Responsibilities
- Design and implement robust, scalable, and reliable data pipelines and systems that handle large volumes of data, empowering both product features and data-driven decision-making across the company.
- Architect, build, and own large-scale data pipelines and data lakes (Spark/Databricks) that ingest and process billions of daily events into reliable, decision-grade datasets.
- Design and implement solutions that keep data available, secure, and scalable across the platform, enabling both real-time and batch processing.
- Own data quality, freshness, and reliability for the foundational datasets other teams depend on, with automated checks, monitoring, and alerting.
- Build the ETL frameworks and tooling that let other Data teams and data scientists self-serve and model core business entities into clean, well-documented, reusable datasets.
- Partner with product teams, back-end engineers, ML engineers, and Data Science to turn business questions into high-impact data products behind business-critical features, research, and experimentation.
- Collaborate with leadership to shape the team’s charter and technical roadmap.
Benefits
- Excellent health care (including a wide range of medical, dental, vision, mental health, and fertility benefits)
- Disability and life insurance options
- 401(k) matching
- Paid parental leave
- 20 days of paid time off per year
- 12 days of paid holidays per year
- Two floating holidays per year
- Flexible sick time
- Generous stipends (including those for caregiving, pet care, wellness, your home office, and more)
- Annual professional development budget and opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
