Data Engineer at Karbon

, , United States -

Full Time

Start Date

Immediate

Expiry Date

16 Apr, 26

Salary

125000.0

Posted On

16 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Snowflake, dbt, Fivetran, Stitch, Real-time Processing, Customer Success, Analytics, Data Quality, Problem-solving, SaaS Metrics, Cloud Platforms, Event Tracking, Product Analytics, Databricks

Industry

Software Development

Description

About Karbon Karbon is the global leader in practice management software for growth-minded accounting firms. We provide an award-winning, highly collaborative cloud platform that streamlines work and communication, enabling the average accounting firm using Karbon to save 18.5 hours per week, per employee. We have customers in 34 countries and have grown into a globally distributed team, with our people based throughout the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well-funded, ranked #1 on G2, have a fantastic team culture built on our values, are growing rapidly, and making a global impact. Role Overview We're seeking a mid-level Data Engineer to join our growing data team and drive customer-focused analytics initiatives. This role will own end-to-end data infrastructure while maintaining a strong focus on understanding and improving customer outcomes. You'll work closely with Customer Success, GTM, and Product teams to ensure our data systems enable actionable insights that drive customer retention and growth. About the Role Data Infrastructure & Engineering (60%) Design, build, and maintain scalable data pipelines using Snowflake, dbt, Fivetran, Stitch and Snowpipe Implement real-time streaming architectures to reduce data latency from hours to minutes Manage schema evolution and data migrations for critical business entities Optimize data warehouse performance and cost efficiency Build robust monitoring and alerting systems for data pipeline health Ensure data quality, reliability, and governance across all systems Customer-Focused Analytics (20%) Develop analytics frameworks to measure customer success and product adoption Build cohorted churn analysis models to identify retention patterns Create performance metrics that correlate with customer outcomes Partner with Customer Success team to understand data needs for customer health scoring Support implementation team analytics to improve onboarding effectiveness Cross-Functional Collaboration (10%) Work with Product teams to instrument new features and track adoption Collaborate with Customer Success on quarterly business review data requirements Support sales and marketing teams with pipeline and funnel analytics Document data models and maintain technical specifications Mentor junior team members and contribute to best practices AI-Enabled Analytics & Tooling (10%) Leverage AI-powered tools to improve development velocity, data quality, and customer insight generation across the analytics stack. Use AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to accelerate SQL, dbt, and pipeline development while maintaining high standards for accuracy and maintainability Apply AI-supported techniques for anomaly detection, data quality monitoring, and schema exploration in customer and product data Enable AI-driven insights within analytics platforms to surface trends, risks, and early indicators of churn or onboarding friction Partner with analytics and business teams to support emerging predictive and proactive customer analytics use cases About You 3-5 years of experience in data engineering or analytics engineering roles Strong proficiency in SQL and experience with modern data warehouses (Snowflake preferred) Experience with dbt for data transformation and modeling Familiarity with streaming data architectures and real-time processing Understanding of SaaS metrics (churn, retention, product adoption, customer health) Experience working with customer-facing teams (Customer Success, Implementation, Product) Strong problem-solving skills and attention to data quality Preferred Qualifications Experience in B2B SaaS or professional services software environments Knowledge of customer success platforms and methodologies Familiarity with event tracking and product analytics tools Experience with cloud platforms (AWS/Azure) and infrastructure as code Understanding of accounting/finance business processes (bonus) Previous experience supporting non-technical stakeholders with data needs Databricks experience strongly preferred - our data platform will be migrating from Snowflake to Databricks in the coming months Technical Stack Data Warehouse: Snowflake (transitioning to Databricks) Transformation: dbt (data build tool) ETL: Fivetran, Stitch Orchestration: Snowpipe, scheduled jobs Analytics Platform: Netspring Collaboration: Linear, Slack, documentation tools Why work at Karbon? Gain global experience across Australia, New Zealand, UK, and Canada Strong benefits package including: Flexible Time Off with an encouraged 4 weeks use per year Company paid medical for you and eligible spouse/partner and dependents Paid dental and vision and eligible spouse/partner and dependents 401(k) with company matching Flexible Spending Account Up to 8 weeks paid parental leave Work-from-home stipend Work with (and learn from) an experienced, high-performing team A collaborative, team-oriented culture that embraces diversity, invests in development and provides consistent feedback Be part of a fast-growing company that firmly believes in promoting high performers from within As we hire across various locations within the USA we are required by law to include a reasonable estimate of the compensation range for this role. The range provided is broad and takes into consideration a wide range of factors that are reviewed when making a hiring decision, such as physical location/cost of living in that location, years of experience, skills, and other business needs. It is not typical for a candidate to be hired at or near the top of the pay range and each compensation decision is dependent on each individual case. The base salary is one component of the total compensation package, which for some roles may include a target bonus, for some roles very competitive equity grant, and very generous benefits. While we believe competitive compensation is a critical aspect of you deciding to join us, we do hope you also spend time considering why our mission, purpose and values are right for you. We are creating something transformational here, and we hope you are as excited about the future as we are! The estimated base salary range for this role is: $116,000—$125,000 USD Karbon embraces diversity and inclusion, aligning with our values as a business. Research has shown that women and underrepresented groups are less likely to apply to jobs unless they meet every single criteria. If you've made it this far in the job description but your past experience doesn't perfectly align, we do encourage you to still apply. You could still be the right person for the role! We recruit and reward people based on capability and performance. We don’t discriminate based on race, gender, sexual orientation, gender identity or expression, lifestyle, age, educational background, national origin, religion, physical or cognitive ability, and other diversity dimensions that may hinder inclusion in the organization. Generally, if you are a good person, we want to talk to you. 😛 If there are any adjustments or accommodations that we can make to assist you during the recruitment process, and your journey at Karbon, contact us at people.support@karbonhq.com for a confidential discussion. At this time, we request that agency referrals are not submitted for this position. We appreciate your understanding and encourage direct applications from interested candidates. Thank you!

Responsibilities

The Data Engineer will design, build, and maintain scalable data pipelines while ensuring data quality and governance. They will also develop analytics frameworks to measure customer success and collaborate with cross-functional teams.