Snowflake Data Engineer

Job Description

About Company: The healthcare industry is the next great frontier of opportunity for software development, and Health Catalyst is one of the most dynamic and influential companies in this space. We are working on solving national-level healthcare problems, and this is your chance to improve the lives of millions of people, including your family and friends. Health Catalyst is a fast-growing company that values smart, hardworking, and humble individuals. Each product team is a small, mission-critical team focused on developing innovative tools to support Catalyst’s mission to improve healthcare performance, cost, and quality. About the Role We are seeking a Senior Snowflake Data Engineer with 5–6 years of experience in data engineering and a strong specialization in the Snowflake Data Cloud. This role is ideal for a candidate who thrives in building high-volume, scalable data pipelines and enjoys working with modern data lake and warehouse architectures—particularly Snowflake—leveraging its core internals and best practices to deliver optimized, high-performance data solutions. Key Responsibilities • Lead the design, implementation, and maintenance of Snowflake-centric ELT pipelines, ensuring optimal warehouse usage, performance, and cost efficiency. • Architect Snowflake data models using best practices in clustering, partitioning, and schema design to support analytics at scale. • Utilize advanced Snowflake features such as Streams, Tasks, Snowpipe, Materialized Views, and Time Travel for real-time and batch processing needs. • Ensure secure and compliant data practices through RBAC, masking policies, and low-level access controls in Snowflake. • Design data ingestion pipelines from diverse sources using Apache Kafka and other streaming frameworks. • Collaborate with stakeholders to identify data needs, model analytical solutions, and deliver trusted datasets via Snowflake. • Optimize Snowflake queries and warehouse configurations for both performance and cost across large-scale data volumes (hundreds of millions of rows/day). • Orchestrate and automate data workflows using tools like AWS Step Functions, Airflow, or dbt. • Monitor, troubleshoot, and continuously improve pipeline and warehouse performance. • Enforce data quality, lineage, and governance standards across the Snowflake environment. Required Skills • 5–6 years of overall data engineering experience with at least 2–3 years of deep, hands-on Snowflake experience. • Expertise in o Snowflake internals: performance tuning, clustering keys, result caching, auto suspend/resume. o Data ingestion and transformation using Snowpipe, Streams & Tasks, Materialized Views, and UDFs. o Security and governance in Snowflake (RBAC, secure views, data masking). • Strong SQL skills and understanding of dimensional and normalized data modeling. • Proficiency with AWS Glue, Apache Spark, and PySpark for building ELT/ETL pipelines. • Experience with data lakes (Delta Lake on S3 or similar). • Exposure to event streaming and data ingestion using Apache Kafka or equivalent. • Familiarity with orchestration tools such as Airflow, dbt, Dagster, or AWS Step Functions. • Knowledge of CI/CD practices and version control with Git. Nice to Have • Snowflake Certification (SnowPro Core or Advanced Architect). • Experience with Infrastructure as Code (Terraform, CloudFormation). • Exposure to DataOps and monitoring tools (CloudWatch, Datadog). • Working knowledge of Databricks, including Delta Lake, notebooks, and Spark runtime optimization. • Understanding of serverless and containerization technologies (AWS Lambda, Docker). Why Join Us? • Work on impactful, high-scale data engineering challenges in a modern cloud environment. • Be the Snowflake expert and evangelist within a fast-moving data team. • Enjoy autonomy, a high-ownership culture, and continuous learning. • Flexible remote options and opportunities for career growth. Show more Show less