Senior Data Engineer

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.

• 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.

• 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.

• 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.

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.