Data Engineer

About The Role

The role is responsible for designing, building, and optimizing the core data infrastructure and pipelines that power both business intelligence and machine learning applications. This includes developing robust ETL/ELT processes that ingest high-volume, real-time, and batch data from diverse sources into a centralized data warehouse.

The team operates in a hybrid model out of Seattle, WA, collaborating closely with software engineers, product managers, and data scientists to ensure data accessibility, reliability, and high performance across the entire organization.

Key Responsibilities

• Design, develop, and maintain scalable, automated ETL/ELT data pipelines using Apache Airflow and dbt
• Optimize data warehouse performance in Snowflake, implementing clustering keys, materialization strategies, and cost-control measures
• Build robust data ingestion pipelines from external APIs, production microservices databases (PostgreSQL, MongoDB), and streaming sources (Kafka, Kinesis)
• Implement data quality frameworks and automated testing suites to ensure accurate and trusted datasets for downstream analytics
• Collaborate with analytics engineers to design and implement clean, structured dimensional models (Star Schema) that simplify reporting
• Write clean, modular, and well-tested Python and SQL code, adhering to software engineering best practices including continuous integration and deployment (CI/CD)

What We Are Looking For

• 3-6 years of experience in data engineering, data infrastructure, or a closely related quantitative field
• Advanced, production-grade SQL proficiency and extensive experience writing structured, scalable Python for data manipulation
• Hands-on experience managing modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestrators like Apache Airflow, Prefect, or Dagster
• Strong understanding of data modeling concepts, including dimensional modeling (Kimball methodology) and schema design
• Experience with version control (Git) and deploying pipelines via modern CI/CD practices
• Bonus: Experience with infrastructure as code (Terraform), streaming data platforms (Kafka/Flink), or BI tool administration (Looker, Tableau)