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Salary: USD 125,000 - 140,000 / annual
Artie is a fully-managed real-time data streaming platform that replicates production databases into data warehouses and lakes with zero maintenance. The company powers mission-critical use cases including fraud monitoring, inventory visibility, customer-facing analytics, and AI/ML workloads, and is trusted by teams like Substack, Alloy, and ClickUp. Artie raised a Series A from top-tier investors including Standard Capital, Y Combinator, General Catalyst, and the founders of Dropbox and Mode.
You will be Artie's first Solutions Engineer, responsible for scaling the pre-sales and post-sales enterprise motion. This is a technical role focused on deep customer engagement, not demo delivery. You will own the technical win by running structured proof-of-value engagements with clear success criteria, partnering with account executives to drive deals from discovery through close, and serving as the trusted technical voice with staff engineers and data architects.
Key responsibilities include understanding customer data architectures (databases, pipelines, cloud and on-premise infrastructure), mapping those architectures to Artie's capabilities (CDC, Kafka, cloud data warehouses, replication patterns), and whiteboarding solutions. You will deliver compelling technical presentations that translate complex concepts like log-based CDC and schema evolution into business impact for mixed audiences of engineers and executives. You'll surface field insights to Product and Engineering to sharpen the roadmap, work closely with Engineering on post-sale customer handoffs, and help define what great looks like for the SE function at Artie.
REQUIREMENTS:
- 5+ years of experience as a Solutions Engineer or Sales Engineer in enterprise or similarly complex environments
- Deep familiarity with at least one RDBMS (Oracle, SQL Server, PostgreSQL, DB2) including basic troubleshooting, logging/archiving, and query plan analysis
- Working knowledge of at least one cloud data warehouse (Snowflake, Iceberg, Databricks, Redshift)
- Comfortable in the data stack with knowledge of tools like Kafka, dbt, and BI platforms
- Experience with cloud infrastructure (AWS, GCP, or Azure) and networking knowledge sufficient for technical solutioning conversations
- Strong communication skills to adjust depth and framing for data engineers and VPs of Engineering in the same meeting
- Structured and rigorous approach to running POCs as projects
- Low ego and high curiosity; preference for understanding customer problems over pitching canned demos
