Design, build, and maintain scalable Snowflake data pipelines for Product and Data Science teams, with ownership of cost optimization, performance tuning, and Streamlit dashboards.
168k – 242k
HybridData Engineering
About the role
Responsibilities
Design, develop, and maintain scalable data pipelines using Snowflake and cloud technologies
Own and manage cost optimization for Product and Data Science team operations
Optimize pipelines for performance, reliability, and efficiency
Build and maintain dashboards and reporting platforms using Streamlit
Collaborate with cross-functional teams to gather requirements and deliver robust data solutions
Work with modern AI tools, including Cursor, to accelerate development
Implement data governance, security best practices, and access control policies
Develop and enforce data quality checks, SLA monitoring, and dependency tracking
Utilize version control systems (e.g., Git) and manage deployment workflows
Proactively troubleshoot and resolve data-related issues
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related field
Proven hands-on experience with Snowflake, including data modeling, ETL/ELT development, and performance tuning
Advanced proficiency in Python, with experience scripting and automating data workflows
Proven experience working with Spark for large-scale data processing
Demonstrated experience building Streamlit applications integrated with Snowflake
Strong command of SQL for complex data querying and analysis
Familiarity with cloud platforms such as AWS, Azure, or GCP, and services like S3, Redshift, or BigQuery
Strong problem-solving and communication skills
Ability to thrive both independently and as part of a collaborative, fast-paced team environment
Preferred Qualifications
Master’s degree in Computer Science, Engineering, or a related discipline
Strong understanding of data warehousing principles, architecture, and best practices
Experience working within Agile development frameworks (e.g., Scrum, Kanban)
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