Data Engineering Lead

SuperDial•Burlingame, CA

1d•$185,000 - $240,000

About The Position

As Data Engineering Lead, you will own SuperDial’s data platform end-to-end. You will be responsible for scaling the warehouse, pipelines, and analytics workflows that power product decisions, go-to-market execution, and company-wide planning. This role is ideal for someone with a strong analytics engineering foundation who has grown into data platform ownership and wants to operate as a true lead, leveraging data analytics and insights proactively to help shape the direction of SuperDial. About the Role: 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 30 / 60 / 90 Day Expectations 30 Days Ramp on SuperDial’s product, data sources, and existing warehouse Audit current dbt models, pipelines, orchestration, and reporting Understand platform data needs and performance considerations Ship early improvements to data quality, reliability, or documentation Build strong relationships with Product, Platform, GTM, and Finance 60 Days Take full ownership of the data warehouse and analytics workflows Improve or refactor core dbt models to support consistent metrics Strengthen orchestration, testing, and monitoring across pipelines Partner with engineering teams on platform-oriented data use cases Deliver dashboards or analyses that materially improve decision-making 90 Days Establish a scalable data architecture and analytics engineering strategy Proactively identify gaps in data availability, quality, or performance Improve reliability and maintainability of data workflows end-to-end Act as the go-to owner for data strategy and execution Lay the groundwork for future platform-oriented or high-scale data systems About You: 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 Why SuperDial True ownership over the company’s data and analytics foundation High-impact role influencing product, platform, and GTM strategy Small, high-caliber team solving meaningful problems in healthcare Competitive compensation and meaningful equity Opportunity to shape and grow the data function over time Who we are: SuperDial is transforming AI in healthcare by building scalable, AI-powered solutions that optimize revenue cycle management. Join us and help shape the future of AI in healthcare! The base salary for this role ranges from $185,000-$240,000, depending on experience, skill set, and fit. We also offer equity and benefits as part of our total compensation package. Final offers may vary based on experience and qualifications - we’re always open to exceptional talent.

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.)
Strong ownership mindset with comfort driving ambiguous initiatives end-to-end
Clear communication skills and ability to partner with technical and non-technical stakeholders

Nice To Haves

Experience working with high-performance or specialized data systems (e.g., ClickHouse or similar) is a plus

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