Senior AI-First Data Engineer

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