Data Engineering Lead overseeing data warehouse, pipelines, and analytics workflows at SuperDial. Driving data strategy and execution for impactful healthcare solutions.
Responsibilities
Own and scale SuperDial’s data warehouse and analytics stack
Lead development of robust dbt-based transformation layers that power trusted metrics
Design and maintain data pipelines that integrate product, GTM, and platform data
Manage and evolve data engineering workflows, orchestration, and dependencies
Partner with platform and product teams to support operational and analytical data needs, including performance-intensive use cases
Build clean, well-documented data models that serve as the source of truth across the business
Create high-impact dashboards and analyses that inform revenue, product usage, and customer behavior
Implement data quality checks, monitoring, and alerting to ensure reliable, decision-ready data
Establish best practices for modeling, testing, and analytics development as the company scales
Requirements
2+ years of experience in Analytics Engineering, Data Engineering, or BI Engineering roles
Deep hands-on experience with dbt, especially incremental models, and modern analytics engineering workflows
Very strong SQL skills and a track record of designing durable data models
Prior ownership of a production data warehouse in a scaling environment
Experience managing data pipelines, orchestration, and workflow dependencies
Proficiency with Python and orchestration tools such as Airflow or similar
Familiarity with cloud data platforms and infrastructure, and NoSQL databases (like Google Cloud Firestore, Mongo, etc.)
Experience working with high-performance or specialized data systems (e.g., ClickHouse or similar) is a plus
Strong ownership mindset with comfort driving ambiguous initiatives end-to-end
Clear communication skills and ability to partner with technical and non-technical stakeholders.
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