Data Engineering Team Lead

Join DataSnipper as a Data Engineering Team Lead, where you will lead the Data Platform team and make critical decisions that impact the entire company. You will be responsible for the people, roadmap, and technical direction of the team, while also staying hands-on with the work. The Data Platform team focuses on reliable infrastructure, self-service data access, and customer-facing dashboards. You will work with a variety of technologies and have the opportunity to shape the team's future.

• Lead a team of data engineers to own the systems that capture usage events across products, ensuring data integrity and reliability.
• Set clear goals for the team, foster a culture of experimentation and learning, and provide regular feedback to help engineers grow.
• Collaborate with cross-functional teams to shape the team's roadmap, turning vague data requests into scoped, well-defined work.

• Experience on a cloud platform at the infrastructure level
• An empowerment style of leadership: you lead through outcomes and trust, not by assigning tasks
• Early evidence of growing engineers and raising a team's bar, and the willingness to address performance issues directly
• Bias to action, a strong sense of ownership, and the judgment to prioritize when demand exceeds capacity
• Excellent communication in English and genuine comfort working directly with non-technical stakeholders
• At least 1 year leading engineers as a team lead or engineering manager, including 1:1s, feedback, goal setting, performance, and development
• Production experience with a cloud data warehouse and a modern transformation framework
• 7+ years in data engineering or a closely related backend/platform role, with a track record of owning a data platform area end to end
• Strong product aptitude: you can challenge the framing of a request, translate stakeholder needs into technical solutions, and make roadmap trade-offs
• Solid data modeling fundamentals and the ability to defend a modeling decision to both engineers and business stakeholders
• Experience with event-driven / streaming ingestion and its failure modes: schema drift, duplication, late data, backfills
• Experience operating data systems in production with a focus on reliability, observability and maintainability
• Deep SQL and strong Python, including query optimization and performance tuning on a cloud warehouse
• Experience hiring engineers or helping a team expand its scope
• Experience with Terraform, Docker and data governance at scale
• Experience with product analytics tooling and warehouse-native BI
• Reverse ETL experience and familiarity with CRM data models or customer success platforms
• Experience building data products for AI or agent consumption: semantic layers, metrics layers, MCP servers, or governed self-service access
• Experience in a startup or scale-up, ideally as an early member of a data team