About Karbon
Karbon is the global leader in AI-powered practice management software for accounting firms. We provide an award-winning cloud platform that helps tens of thousands of accounting professionals work more efficiently and collaboratively every day. With customers in 40 countries, we have grown into a globally distributed team across the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well-funded, ranked #1 on G2, growing rapidly, and have a people-first culture that is recognized with Great Place To Work® certification and on Fortune magazine's Best Small Workplaces™ List.
Karbon is transforming how modern businesses manage financial and operational data. As a Senior Data Engineer, you'll be a hands-on technical engineer designing and executing critical data platform initiatives — building the scalable, secure foundations that power AI-driven insights, real-time analytics, and next-generation data experiences serving thousands of customers and millions of transactions.
You'll work closely with our Lead Data Engineer and cross-functional teams across analytics, BI, AI, and product.
What You'll Own- Design and implement distributed data pipelines and ETL/ELT infrastructure using Databricks, with medallion architecture (bronze/silver/gold) and clear data contracts and lineage.
- Build multi-tenant data infrastructure with robust isolation, performance, and cost management across cloud regions.
- Lead integration of our AI-powered analytics platform with Databricks, including semantic layers, row-level security, and self-serve governance patterns.
- Design and implement graph-based data structures for relationship mapping and client intelligence, integrating graph databases with Databricks as the source of truth.
- Own the security and governance model: RBAC, PII masking, data residency, compliance monitoring, and tenant isolation.
- Establish data engineering best practices: testing frameworks, CI/CD for data transformations, observability, and incident response.
- Mentor junior engineers, lead architecture discussions, and communicate technical decisions to both technical and non-technical stakeholders.
Required
- 7+ years as a data engineer, with 2+ years in a senior or staff-level role.
- Deep expertise with cloud data warehouses, Databricks preferred.
- Proficiency with ELT/ETL tools (Fivetran or similar) and transformation frameworks such as dbt.
- Strong SQL and query optimisation skills at scale; deep understanding of dimensional modeling, fact tables, and conformed dimensions.
- Experience implementing row-level security and RBAC in data systems, and familiarity with privacy regulations (GDPR, CCPA, HIPAA).
- Infrastructure-as-Code experience (Terraform or equivalent).
- You've shipped data platforms under real operational pressure and understand the trade-offs between perfect architecture and shipping on time.
Highly Valued
- Graph databases (Neo4j, Amazon Neptune, ArangoDB) and graph query patterns.
- Real-time/streaming data (Kafka, Spark Streaming, Fivetran CDC).
- Python or Scala for data processing beyond SQL.
- ML infrastructure, feature stores, or MLOps tooling.
- Multi-tenant SaaS data architectures and isolation patterns.
- Platform: Databricks (core compute and transformation layer)
- Ingestion: Fivetran and custom pipelines
- Transformation: DLT/ dbt with medallion architecture
- Cloud: Microsoft Azure
- Graph: Neo4j / ArangoDB (evolving)
- Observability: Datadog
- IaC: Terraform
Our architecture continues to evolve toward real-time streaming, graph intelligence, and AI-native data experiences. If you care about platform ownership, engineering excellence, and building a team that ships with confidence. You'll feel right at home here.
Why Work at Karbon?- Gain global experience across the USA, Australia, New Zealand, UK, Canada and the Philippines
- 4 weeks annual leave plus 5 extra "Karbon Days" off a year
- Flexible working environment
- Work with (and learn from) an experienced, high-performing team
- Be part of a fast-growing company that firmly believes in promoting high performers from within
- A collaborative, team-oriented culture that embraces diversity, invests in development, and provides consistent feedback
- Generous parental leave
Balance Speed and Quality Engineers are expected to balance delivery speed with a strong commitment to quality, meeting agreed timelines while producing reliable, maintainable, and well-tested solutions. Sound judgment in making trade-offs between velocity and long-term sustainability is essential.
Collaborate Effectively Engineering is collaborative by default. Team members are expected to contribute constructively in design discussions, reviews, and planning, communicate clearly about progress and risks, and support shared team outcomes in both hybrid and distributed environments.
Build and Maintain Systems Engineers are responsible for building new capabilities while maintaining and improving existing systems. This includes designing scalable solutions, reducing technical debt, supporting operational stability, and contributing to continuous improvement.
Operate with Autonomy A high degree of autonomy is expected. Given clear objectives, engineers should independently translate problems into actionable technical approaches, proactively identify improvements, and continuously expand relevant technical expertise.
Ownership and Accountability Ownership is fundamental. Engineers are accountable for the quality, performance, and customer impact of their work from design through post-release support, and are expected to follow through on commitments.
AI-Enabled Engineering AI is reshaping how software is built, and we are committed to leveraging it as a force multiplier for creativity, impact, and capability. Engineers are expected to confidently apply strong technical fundamentals while embracing AI tools and approaches to enhance productivity, problem-solving, and innovation.
Contribute to Team Culture Engineers contribute positively to a culture of professionalism, transparency, low bureaucracy, and mutual respect, strengthening team performance through authenticity, curiosity, and collaboration.
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 [email protected] 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!
Skills Required
- 7+ years as a data engineer, with 2+ years in a senior or staff-level role
- Deep expertise with cloud data warehouses (Databricks preferred)
- Proficiency with ELT/ETL tools (Fivetran or similar) and transformation frameworks such as dbt
- Strong SQL and query optimisation skills at scale; deep understanding of dimensional modeling, fact tables, and conformed dimensions
- Experience implementing row-level security and RBAC in data systems; familiarity with privacy regulations (GDPR, CCPA, HIPAA)
- Infrastructure-as-Code experience (Terraform or equivalent)
- Experience shipping data platforms under operational pressure and making pragmatic architecture trade-offs
- Experience with graph databases (Neo4j, Amazon Neptune, ArangoDB)
- Experience with real-time/streaming data (Kafka, Spark Streaming, Fivetran CDC)
- Programming for data processing in Python or Scala
- Experience with ML infrastructure, feature stores, or MLOps tooling
- Experience designing multi-tenant SaaS data architectures and isolation patterns
Karbon Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Karbon and has not been reviewed or approved by Karbon.
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Fair & Transparent Compensation — Compensation is described as fair and competitive, with performance-based and globally benchmarked pay. Clear salary ranges appear in postings for several roles, supporting transparency across functions and locations.
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Leave & Time Off Breadth — Time away benefits span flexible PTO or substantial annual leave that increases with tenure. Additional options include wellness days, a birthday day, and the ability to buy or sell leave.
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Parental & Family Support — Support includes generous parental leave and enhanced family leave and pay. Special leave entitlements are available for those with dependents.
Karbon Insights
What We Do
Karbon provides a truly collaborative platform for accounting firms to manage workflows, communicate with teams and deliver exceptional client work.
