Senior Data Engineer

The role

As a Senior Data Engineer at Solflare, you’ll build and optimize the data systems that power our product insights and decision-making. You’ll work closely with analysts, product managers, and engineers to design scalable pipelines, improve our data infrastructure, and make reliable data accessible across teams. Our Data team was formed last year and currently has six people working across data analysis and engineering. The foundations are in place, but the team is still new enough for you to have a real impact on how we model data, design our systems, and work as a team.

The scale and variety of our data make this role especially interesting. Since blockchain data is public, we can analyze user behavior at the individual transaction level across the wider Solana ecosystem, not only within Solflare. This gives us a much clearer view of the full user journey, including how people use other wallets and protocols and where new trends are starting to emerge. One of our biggest challenges is connecting on-chain and off-chain data into a coherent user model. A blockchain public key, an app device ID, and an internal user ID all need to be mapped reliably so we can understand the full journey. At the same time, on-chain data volumes are growing quickly, making pipeline performance and query-cost optimization increasingly important.

Our stack includes BigQuery, dbt Cloud, Python, GCP, PostHog, Dune, Metabase, and Tableau. Claude Code is deeply integrated into our day-to-day work, so AI-assisted development is already the norm here, not an experiment. We’re looking for an engineer who can not only build robust data systems, but also help shape the technical direction of a growing Data team. Core

responsibilities

• Data collection & preparation: You’ll gather data from a variety of sources, including databases, APIs, event tracking tools like PostHog and spreadsheets - ensuring it’s accurate, complete, and relevant to the business questions at hand.
• Design and build scalable data pipelines: Develop, maintain, and optimize ETL/ELT processes that collect, transform, and load data from multiple sources into our data warehouse.
• Data infrastructure: Manage and improve our data infrastructure using modern tools and cloud services (GCP), ensuring high availability and low latency.
• Automation & reliability: Automate data workflows, monitoring, and quality checks to ensure data consistency, accuracy, and reliability across all systems.
• Collaboration: Work with analytics and engineering teams to ensure data needs are met; from ingestion to analysis.
• Data integrity: Ensure that data is secure, well-documented, and accessible to those who need it most.