Data Engineer, Foundations

What you'll do

• Architect and own scalable data pipelines and data lakes that process high volumes of events into reliable datasets.
• Improve data availability, security, quality, freshness, and reliability through monitoring, checks, and alerting.
• Develop ETL frameworks and reusable datasets, partnering with product, engineering, ML, and data science teams.

What they ask for

• 3+ years of experience running live production environments, including high-load or data-intensive workflows.
• Proficiency in SQL and Python, with deep hands-on experience in Spark and a modern lakehouse or cloud data warehouse.
• Knowledge of ETL/ELT design patterns, orchestration, data quality frameworks, and CI/CD for data with Git-based deployments.
• Skills in data modeling and data warehouse design, with a strong focus on data quality and observability.

Nice to have

• Experience with high-throughput real-time streaming systems such as Kafka, Flink, or Spark Structured Streaming.
• Experience with distributed systems, lakehouse technologies, infrastructure as code, or building self-serve data products.

Pay

$157K – $245K USD base salary

Tools

Spark, Databricks, SQL, Python, Delta Lake, dbt, Snowflake, Airflow