Data Engineer, Foundations

As a Data Engineer on the Data Foundations team, you will 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. Your work will span areas such as ETL data pipelines, data lakes, performance-oriented data processing, and ETL framework development.

• 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.

• You have 3+ years of experience running live production environments, including high-load or data-intensive workflows, with a focus on uptime and reliability.
• You’re 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).
• You have 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.
• You have data modeling and data warehouse design skills, along with a rigorous approach to data quality and observability.
• You have hands-on experience with modern data storage technologies (for example, Delta Lake, Snowflake, BigQuery, or Redshift).
• You communicate clearly and collaborate well across diverse teams and stakeholders, turning business needs into robust data solutions and trustworthy metrics.
• You bring a strong ownership mindset, taking end-to-end responsibility for the systems you build, from strategy and architecture through production and ongoing reliability.
• You have a strong acumen for scalable, highly complex data processing, think holistically about problems, and raise the bar on your team’s craft.
• You’re a self-starting problem-solver who thinks from first principles, manages priorities across multiple projects, and thrives in a fast-paced, results-driven environment., * You have 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.

Superhuman offers all team members competitive pay along with a benefits package encompassing the following and more:

• 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, and flexible sick time
• Generous stipends (including those for caregiving, pet care, wellness, your home office, and more)
• Annual professional development budget and opportunities

Superhuman takes a market-based approach to compensation, which means base pay may vary depending on your location. Our US locations are categorized into two compensation zones based on proximity to our hub locations.

Grammarly is now part of Superhuman, the AI productivity platform on a mission to unlock the superhuman potential in everyone. The Superhuman suite of apps and agents brings AI wherever people work, integrating with over 1 million applications and websites. The company’s products include Grammarly’s writing assistance, Docs’ collaborative workspace, Mail’s inbox management, and Go, the proactive AI assistant that understands context and delivers help automatically. Founded in 2009, Superhuman empowers over 40 million people, 50,000 organizations, and 3,000 educational institutions worldwide to eliminate busywork and focus on what matters. Learn more at superhuman.com and about our values here., To achieve our ambitious goals, we’re looking for a Data Engineer to join our Data Foundations team and help us build a world-class data platform. Superhuman’s success depends on its ability to efficiently ingest and process over 70 billion daily events to improve our products. This role presents a unique opportunity to experience all aspects of building complex data pipelines and systems, including contributing to the strategy, defining the architecture, and developing and shipping to production.

Superhuman is a compound startup: we build many products as one integrated suite rather than standalone tools. That model creates an unusually rich data opportunity, with signals spanning our full product suite across both consumers and enterprises - and the foundational data you build connects those surfaces.

The Data Foundations team is part of the Data Platform org, powering data needs across Superhuman rather than a single product surface. It owns the foundational datasets and data models that define the fundamental entities of Superhuman’s business, serving as building blocks for the company’s other data teams and data scientists. Behind the scenes, the team develops and runs 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 you build directly shape how efficiently our other Data teams can build their datasets.