Work Experience: 10+ years of experience designing, building, and optimizing scalable data platforms, with strength in Snowflake, Databricks and modern Lakehouse architecture. Prior experience in a pre-sales, consulting, solutions engineering, or technical advisory capacity within an enterprise technology organization is preferred.
- Deep hands-on experience with modern cloud data platforms, particularly Snowflake and Databricks. This includes platform capabilities such as Snowflake's Snowpark, Dynamic Tables, Streams & Tasks, and Snowflake Cortex, as well as Databricks components such as Lakeflow (Connect, Pipelines, and Jobs), Delta Lake, and Unity Catalog.
- Strong data engineering fundamentals: ETL/ELT pipeline design and implementation, data orchestration and workflow automation, batch and streaming processing, and data modeling for analytical and operational workloads.
- Proficiency in SQL and Python sufficient to write, debug, and review production-quality code independently.
- Working fluency in lakehouse and data platform architecture — able to reason through platform tradeoffs and answer architecture-level questions in real time alongside engineering questions, since customers routinely expect both in the same conversation.
- Governance fluency: able to represent data quality, security, and trust topics credibly in customer conversations, while governance strategy and roadmap ownership sit with a dedicated specialist role.
- Practical understanding of how AI workloads — LLMs, RAG, agentic AI — consume enterprise data. The emphasis is on engineering trusted, scalable data foundations, not building AI models.
- Experience integrating and using AI coding assistants and agent tools (e.g., Claude, Copilot, Glean, Snowflake Cortex Code) with cloud data platforms.
- Experience implementing Databricks and Snowflake solutions on Azure, AWS, or Google Cloud.
- Advisory mindset and the ability to lead customers through ambiguous technical challenges: structuring discovery engagements, identifying technical and organizational gaps, evaluating platform tradeoffs objectively, and delivering actionable recommendations.
- Experience supporting a services sales motion in a non-quota-carrying, technical advisory capacity — partnering with account teams to shape and advance service engagements.
- Strong communication skills across audiences — data engineers, architects, IT leadership, and executive stakeholders — tailoring technical depth while maintaining credibility with each.
- Experience with scoping and/or delivering large-scale data platform migrations.
Preferred:
- Experience with additional cloud data platforms such as Google BigQuery, AWS Redshift, or Azure Synapse.
- CI/CD, DevOps, and Infrastructure as Code practices for data platforms.
- Metadata management, lineage tooling, and data observability/monitoring experience.