Senior AI-First Data Engineer

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