Data Foundations Engineer: Scalable Pipelines & Data Lakes

Superhuman; Seattle (WA); Hybrid; USD 157,000 - 245,000; Full time

Benefits offered by this job

Health benefits

401(k) matching

Paid parental leave

20 days PTO + holidays

Job summary

Superhuman is seeking a Data Engineer to join the Data Foundations team. You will design and implement scalable data pipelines and data lakes, enabling product features and data-driven decision-making across the company.

You will own data quality, build ETL frameworks, and collaborate with product, back-end, ML, and data science teams to productionize data and models. This role combines data engineering with data modeling and observability across a fast-paced environment.

Qualifications

• 3+ years in live production data environments.
• Proficient in SQL and Python; Spark experience.
• Experience with Databricks or lakehouse data warehouses.
• Knowledge of ETL/ELT patterns and orchestration tools.
• Data modeling and data warehouse design skills.
• Hands-on with modern storage technologies (Delta Lake, Snowflake, BigQuery, Redshift).
• Clear communication and cross-team collaboration.
• Ownership mindset for end-to-end systems.

Responsibilities

• Architect, build, and own large-scale data pipelines and data lakes (Spark/Databricks).
• Design data platforms that support real-time and batch processing.
• Own data quality, freshness, and reliability for foundational datasets.
• Build ETL frameworks and tooling for self-serve data products.
• Partner with product, back-end, ML, and Data Science teams to deliver data solutions.
• Collaborate with leadership to shape roadmap and strategy.

Skills

SQL

Python

Data modeling

Data warehousing

CI/CD for data

Observability

Tools

Spark

Databricks

Delta Lake

dbt

Snowflake

BigQuery

Redshift

Airflow

Dagster

Terraform

Job description

Superhuman is seeking a Data Engineer to join the Data Foundations team. You will design and implement scalable data pipelines and data lakes, enabling product features and data-driven decision-making across the company.

You will own data quality, build ETL frameworks, and collaborate with product, back-end, ML, and data science teams to productionize data and models. This role combines data engineering with data modeling and observability across a fast-paced environment.