Lead Data Engineer

Lead Data Engineer

Our Engineering Standards 

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. Curiosity, adaptability, and enthusiasm for integrating AI into meaningful product development are essential.

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.

About the Role! 

Karbon is at the cutting edge of AI and data products, and this role sits at the heart of that innovation. Supporting both our AI and Insights teams, you'll play a critical part in delivering features across the Karbon platform. You'll evaluate and evolve our data architecture — with Databricks at its core — identifying opportunities to push our capabilities even further. We're looking for a hands-on builder and strategic thinker who can design scalable, robust, and forward-looking data solutions.

We are seeking an experienced Lead Data Engineer who thrives in a fast paced environment. You will have the unique opportunity to build the new unified data platform to power our suite of AI tools and insight delivery.

What you will own:

• Architecting a unified data platform: Design, implement, and own our new unified data platform on Databricks. You will be instrumental in establishing the Medallion Architecture (Bronze, Silver, Gold layers) using dbt for data modeling and transformations.
• Develop Data Pipelines: Create and manage resilient data pipelines for both batch and real-time processing from various sources in our Azure data ecosystem. This includes building a "hot path" for streaming data and orchestrating complex dependencies using Databricks Workflows.
• Enable Data Integration and Access: Implement and manage data replication processes from Databricks to Snowflake. You will also be responsible for developing a low-latency query endpoint to serve our production Karbon application.
• Champion Data Quality and Governance: Establish best practices for data quality, integrity, and observability. You will build automated quality checks, tests, and monitoring for all data assets and pipelines to ensure trust in our data.
• Advance our MLOps Capabilities: Partner with the AI team to design and implement MLOps frameworks. This includes building pipelines for feature engineering, model training, evaluation, and deployment to accelerate our machine learning lifecycle.
• Implement Robust Security and Governance Practices: Design and enforce a comprehensive security model for the data platform. This includes management of PII and implementing a fine-grained Role-Based Access Control (RBAC) model through IaC
• Collaborate and Mentor: Work within a cross-functional team of AI engineers, analysts, and developers to deliver impactful data products. As a senior member of the team, you will mentor junior engineers, lead by example, and help define our technical standards.

What Sets You Apart (Qualifications) 

• 7+ years of relevant work experience as a data engineer,  with a proven track record of building and scaling data platforms
• Extensive experience with Databricks
• Extensive experience architecting ETL & ELT data migration patterns with strong proficiency in dbt.
• Experience scaling data pipelines in a multi-cloud environment
• Strong proficiency in Python
• Strong proficiency in SQL and a deep understanding of relational DBMS
• Experience with both batch and streaming data technologies
• DevOps experience, including CI/CD, and infrastructure-as-code (e.g., Terraform)
• Experience building and maintaining APIs or query endpoints for application data access
• Proven ability to lead technical projects and mentor other engineers
  
  

It would be advantageous if you have:

• Previous experience with Azure cloud services (Highly desirable)
• DevOps experience is highly desirable
• Practical MLOps experience, such as implementing solutions with MLflow, feature stores, and automated model deployment and evaluation pipelines.

Ideal for engineers who thrive in structured environments, complex domain systems, and enterprise-scale reliability challenges.

Our Core Technology Stack

We build modern, scalable software on a thoughtfully designed stack:

• Frontend: TypeScript and JavaScript across Ember (today), React, and React Native.
• Backend: .NET / C# (Web API, .NET Core) powering distributed services.
• Data: SQL Server with performance and integrity at scale.
• Cloud: Microsoft Azure.
• Observability: Metrics, logging, alerting, and dashboards in Datadog — because we believe you can’t improve what you don’t measure.

Our architecture continues to evolve as we scale. We invest in event-driven systems, well-defined microservices, and containerized deployments (Azure Container Apps) to build resilient, decoupled, and high-performing software.

If you care about clean service boundaries, reliable systems, and shipping 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