Job Summary
The Senior Technical Specialist will be responsible for managing and optimizing Snowflake, DBT, SQL, and Python processes within the organization. They will play a key role in implementing and maintaining efficient data pipelines, ensuring data integrity, and driving data driven decision-making processes.
Key Responsibilities
Design, develop, and maintain data engineering pipelines using Snowflake-native features including Streams, Tasks, and Dynamic Tables.
Build interactive data applications using Streamlit in Snowflake to enable self-service analytics and internal tooling.
Implement AI-powered solutions using the Snowflake Cortex suite, including Cortex Analyst, Cortex Agents, Cortex Search Services, and Cortex LLM functions to address business needs.
Design and maintain data warehouse architectures following sound data modeling principles (dimensional modeling, Data Vault, or equivalent).
Develop and optimize ELT pipelines, ensuring data quality, reliability, and performance at scale.
Monitor, troubleshoot, and optimize pipeline performance and resource utilization within Snowflake.
Skill Requirements
Snowflake (Expert-level): Deep hands-on experience with Snowflake, including Streams, Tasks, Dynamic Tables, Snowpipe, and performance tuning.
Snowflake Cortex Suite: Working knowledge of Cortex Analyst, Cortex Agents, Cortex Search Services, and Cortex LLM functions (Complete, Summarize, Translate, etc.).
Streamlit in Snowflake: Ability to develop and deploy interactive Streamlit applications natively within Snowflake.
Azure Cloud Platform: Foundational understanding of Azure services (Storage, Data Factory, networking) as they integrate with Snowflake.
Data Warehousing & Modeling: Strong understanding of dimensional modeling, star/snowflake schemas, slowly changing dimensions, and related design patterns.
ELT Methodology: Solid experience with ELT patterns, transformation logic, and incremental loading strategies.
Programming: Proficiency in Python or JavaScript (used for UDFs, stored procedures, and automation).
Analytical Skills: Strong ability to analyze data, identify patterns, validate pipeline outputs, and troubleshoot data quality issues.
Communication: Clear and effective verbal and written communication skills; ability to convey technical concepts to non-technical stakeholders.
Work Ethic: Self-motivated, detail-oriented, and committed to delivering high-quality work consistently.