Senior Data Engineer (AI-First)

Mission

Data is becoming one of Smartcat’s most strategic assets.

As a Senior Data Engineer, you will help build the intelligence layer that powers decision-making, AI agents, automation, and business operations across the company.

This is not a traditional data engineering role focused solely on pipelines and dashboards.

You will help transform Smartcat’s data platform into an AI-native foundation where business data becomes discoverable, actionable, and consumable by both humans and AI agents. You will partner closely with Data, Product, Engineering, GTM, and AI teams to create a modern data ecosystem that enables self-service analytics, intelligent automation, and agent-driven decision support.

We're currently building a modern greenfield cloud data platform and transitioning from legacy on-premise and batch architectures toward cloud-native, streaming-first infrastructure. This is an opportunity to have a significant impact on how Smartcat scales its data capabilities over the next several years. The role aligns directly with Smartcat's transformation into an AI-native company and agentic platform.

Outcomes

Evolve our architecture into Next Generation Data Platform

• Continue building our data architecture into scalable cloud-native AI-first architecture
• Lead the transition from batch-oriented pipelines to near real-time and streaming data systems
• Improve reliability, observability, governance, and performance across the data stack
• Establish engineering standards and best practices for data development

Turn Business Data into AI-Ready Assets

• Build data products that can be consumed by AI agents, analytics systems, and business users
• Enable semantic layers, metadata management, and knowledge structures that make data more accessible and actionable
• Create foundations for agent-driven reporting, forecasting, and business intelligence
• Help transform business metrics from static dashboards into living operational systems

Drive AI-First Data Engineering

• Use AI to accelerate development, testing, documentation, monitoring, and operational workflows
• Design systems that allow AI agents to query, understand, and act on business data safely
• Evaluate emerging AI technologies and identify opportunities to increase productivity across the data organization
• Help establish Smartcat as an AI-native data organization, leveraging AI to increase productivity and talent density across the company.

Improve Business Intelligence & Data Accessibility

• Make analytics simpler and more accessible for non-technical stakeholders
• Improve usability and adoption of BI tools such as Omni and future AI-powered analytics experiences
• Enable self-service analytics without sacrificing governance or data quality
• Reduce time-to-insight across Product, Revenue, Marketing, Customer Success, and Finance teams

Lead Through Technical Excellence

• Raise the engineering bar through mentorship, code reviews, architecture leadership, and knowledge sharing
• Influence technical direction across Data Engineering and Analytics Engineering
• Partner with stakeholders to align platform investments with business priorities

Requirements

Experience

• 6+ years of experience in Data Engineering, Analytics Engineering, or a related field
• Proven track record designing and operating modern cloud data platforms
• Experience in high-growth SaaS environments
• Experience working with both technical and business stakeholders
• Demonstrated ability to lead complex projects from design through delivery

Technical Expertise

Strong hands-on experience with:

• Databricks
• dbt
• Airflow
• Python for data engineering
• SQL and data modeling
• Data warehousing architectures
• Data quality frameworks
• Data governance practices
• Data orchestration and integration

Experience with:

• Streaming architectures and event-driven systems
• Business intelligence platforms
• Product analytics platforms
• CRM and customer data platforms

AI-First Mindset (Required)

You should be able to demonstrate:

• AI-powered development workflows
• Automation of repetitive engineering tasks
• Use of AI for debugging, testing, architecture exploration, and documentation
• Experience integrating AI into data or analytics workflows
• Curiosity about agentic systems and the future of AI-enabled data platforms
• Evidence of measurable productivity gains through AI adoption

Candidates may also have:

• Experience building AI-native data products
• Experience with semantic layers, RAG systems, vector databases, or knowledge graphs
• Experience enabling AI agents to consume operational business data