Senior Solution Engineer

This is a fully remote position, open to applicants in North Carolina, +1 more state.

📋 Description

• Present the vision of Snowflake's Data Cloud to leaders in data engineering, analytics teams, data scientists, and executive stakeholders.

• Conduct hands-on technical assessments of customers' data architectures, focusing on pipelines, warehouses, lakehouses, and identifying governance gaps.

• Develop Snowflake-native strategies based on customer needs.

• Create and present customized demonstrations and proofs of concept utilizing Snowflake's data platform.

• Highlight data engineering features, including Snowpark, Dynamic Tables, and Snowpipe.

• Demonstrate AI/ML functionalities, such as Cortex AI and the machine learning model registry.

• Exhibit data-sharing features, including Data Clean Rooms and Marketplace listings.

• Guide clients through architectures related to Data Mesh, Data Lakehouse, real-time streaming, and unified governance.

• Convert business challenges into tangible Snowflake data solutions.

• Collaborate with sales teams and channel partners to facilitate successful sales cycles.

• Work alongside Product Management, Engineering, and Field teams to integrate customer architecture patterns into the product roadmap.

⛳️ Requirements

• Must reside in North Carolina or South Carolina.

• 3–8 years of relevant experience in a pre-sales or solutions architecture role concentrating on data platforms.

• Profound hands-on knowledge of SQL and Python.

• Familiarity with cloud data warehouse and data lakehouse architectures.

• Experience with ETL/ELT tools such as dbt, Fivetran, Informatica, and Spark.

• Knowledge of streaming platforms like Kafka and Kinesis.

• Proficiency with orchestration tools, including Airflow and dbt Cloud.

• Experience with BI tools, including Tableau, Looker, and Power BI.

• Familiarity with cloud providers AWS, Azure, and GCP.

• Proven ability to perform in-depth data architecture discovery and relate insights to a Snowflake reference architecture.

• Experience in promoting AI and ML capabilities, encompassing feature stores, model training pipelines, LLM-powered applications, and AI governance.

• Strong grasp of data governance, quality, and compliance challenges, including GDPR, CCPA, and PCI.

• Capability to quantify business benefits, such as cost reduction, time-to-insight, pipeline reliability, and data product monetization.

• Demonstrated experience using AI code generation tools.

• A university degree in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience.

• Experience working with GSIs on large data platform initiatives is advantageous but not essential.

🏝️ Benefits

• Competitive base salary.

• Variable compensation.

• Equity.

• 401(k).

• Health, dental, and vision insurance.

• Flexible paid time off.

• Professional development budget.

• Remote work arrangement.