Senior Solution Engineer

As a Senior Solution Engineer at Snowflake, you will partner closely with Sales, Product, Engineering, and Marketing to drive customer success across a wide range of industries.

You will act as a trusted technical advisor, translating complex business challenges into scalable, secure, and high‑performing data platform solutions. This role requires a blend of deep technical expertise, strong commercial acumen, and excellent customer engagement skills.

You will lead technical discovery, architect solutions, deliver compelling demonstrations, and guide customers through proof‑of‑concepts and production adoption. You are equally comfortable engaging with technical teams and senior business stakeholders, and you thrive in a fast‑paced, collaborative environment.

Responsibilities

• Present Snowflake technology, architecture, and vision to both executive and technical stakeholders at prospective and existing customers.

• Lead technical discovery sessions to understand customer requirements, current architecture, and success criteria, and translate them into solution designs and implementation approaches.

• Design scalable, secure, and cost‑efficient architectures that address customer use cases across data ingestion, storage, transformation, analytics, AI/ML, and data applications.

• Build and deliver tailored demos, prototypes, and proof‑of‑concepts that showcase differentiated value and drive technical validation.

• Collaborate with Account Executives on account strategy, value messaging, and technical win plans to progress opportunities to successful closure.

• Partner with Product and Engineering to provide field feedback on customer requirements, product gaps, and emerging use cases, helping to influence the product roadmap.

• Develop and maintain a deep understanding of the competitive landscape and how to position our solutions effectively against alternative technologies and approaches.

• Act as a trusted advisor to customer technical teams, building strong relationships and providing guidance on best practices, governance, security, and operationalization.

• Support customer onboarding and initial implementation, ensuring a smooth handover to customer success and services teams.

Minimum Requirements

• Extensive experience in a customer‑facing solution engineering / sales engineering / pre‑sales or equivalent technical consulting role.

• Strong hands‑on competency with modern data platforms and architectures, including:

• Data warehouse / data lake / lakehouse concepts

• Data ingestion, ETL/ELT, and data integration patterns

• Analytics, BI, and/or data applications

• Solid proficiency in SQL and at least one general‑purpose or data‑processing language (e.g. Python, Java, Scala, or Spark).

• Experience designing and/or operating solutions on at least one major cloud platform (e.g. AWS, Azure, or GCP).

• Strong stakeholder management, communication, and storytelling skills, with the ability to clearly articulate value and differentiation.

• Track record of collaboration with sales teams to drive opportunities from qualification to close.

Strongly Desired

• Hands‑on experience with large‑scale data platforms (e.g. traditional data warehouses, MPP databases, Hadoop‑based systems, or cloud‑native data platforms).

• Experience working with data pipelines and orchestration tools (e.g. Airflow, dbt, cloud‑native services, or similar).

• Exposure to analytics / BI tools (e.g. Tableau, Power BI, Looker, or equivalent).

• Familiarity with data modeling concepts (e.g. dimensional/OLAP modeling, data vault, or similar).

• Experience with enterprise SaaS or platform‑as‑a‑service solutions, particularly in data, analytics, or AI.

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.

Added Bonus

• Experience with lakehouse architectures and modern data stack components.

• Exposure to Data Science / AI / ML / Generative AI workloads and associated tooling.

• Familiarity with governance, security, and compliance considerations for data platforms (e.g. access control, data protection, audit, and monitoring).

• Contributions to technical communities (e.g. meetups, conferences, open‑source projects, blogs, or public talks).