$140,000–$185,000 Per Year
Amazon Web Services (AWS), Apache Spark, Budgeting, Channel Strategies, Cloud Computing, Communication Skills, Cross-Functional, Data Management, Data Modeling, Data Quality, Engineering, GCP (Good Clinical Practices), Mentoring, Microsoft Windows Azure, Python Programming/Scripting Language, Quality Management, SQL (Structured Query Language), Scalable System Development, Snowflake Schema, Software as a Service (SaaS), Training Data Sets, Warehousing
Our client, an analytics-driven SaaS company, is hiring a Senior Data Engineer to design and own their data platform. You'll build scalable batch and streaming pipelines in Python and Spark, model data in Snowflake, and partner with analytics and ML teams to make trustworthy data easy to use across the company.
Responsibilities
- Design and build batch and streaming data pipelines in Python/Spark
- Model and govern the warehouse layer in Snowflake (or BigQuery)
- Partner with analytics and ML teams to ship clean, reliable datasets
- Improve data quality, lineage, and observability across the stack
- Mentor mid-level data engineers and review their designs
- Lead architecture decisions for the data platform roadmap
Requirements
- 5+ years of data engineering experience
- Strong Python and SQL; production Spark or similar distributed compute
- Hands-on Snowflake, BigQuery, or Redshift in production
- Workflow orchestration experience (Airflow, dbt, Prefect, Dagster)
- Cloud experience (AWS, GCP, or Azure)
- Strong design and communication skills for cross-functional partners
Benefits
- Competitive base salary plus annual bonus
- Medical, dental, and vision
- 401(k) with employer match
- Remote-first culture and flexible PTO
- Learning and conference budget