Why This Job is Featured on The SaaS Jobs
This Data Engineer role sits at a common inflection point for SaaS companies: turning product, customer, and go-to-market data into reliable infrastructure that can support retention-focused decision making. The remit spans modern warehouse and transformation tooling (Snowflake, dbt, Fivetran/Stitch) alongside lower-latency patterns, which signals a shift from batch reporting toward operational analytics that SaaS teams increasingly rely on.
From a SaaS career perspective, the work builds fluency in the metrics and feedback loops that define subscription businesses—adoption, churn, and customer health—while keeping ownership anchored in engineering fundamentals like schema evolution, observability, and governance. The mention of an upcoming platform migration also points to experience that translates across SaaS environments: evaluating trade-offs, managing change without breaking downstream consumers, and keeping data quality stable as systems evolve.
This is best suited to a mid-level engineer who prefers end-to-end accountability and frequent cross-functional partnership rather than a narrow, back-office pipeline role. It will appeal to professionals who like balancing technical rigor with stakeholder-facing problem framing, and who want their work to connect directly to how a SaaS product retains and grows its customer base.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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
