Senior AI-First Data Engineer

WHY YOU SHOULD JOIN OUR TEAM

You'll help build the intelligence layer that powers decision-making, AI agents, automation, and business operations across Smartcat — on a greenfield cloud data platform, transitioning from legacy on-premise, batch architectures to cloud-native, streaming-first infrastructure. This is a chance to shape how Smartcat scales its data capabilities over the next several years, directly aligned with the company's transformation into an AI-native, agentic platform.

MISSION

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 — partnering across Data, Product, Engineering, GTM, and AI teams to enable self-service analytics, intelligent automation, and agent-driven decision support.

OUTCOMES

Evolve Smartcat's data architecture into a scalable, cloud-native, AI-first platform, leading the transition from batch-oriented pipelines to near real-time and streaming systems

Improve reliability, observability, governance, and performance across the data stack, establishing engineering standards and best practices

Turn business data into AI-ready assets — building data products consumable by AI agents, analytics systems, and business users, with semantic layers and metadata management

Use AI to accelerate development, testing, documentation, monitoring, and operational workflows, and design systems that let AI agents query, understand, and act on business data safely

Improve business intelligence and data accessibility — making analytics simpler for non-technical stakeholders and enabling self-service without sacrificing governance or quality

Raise the engineering bar through mentorship, code reviews, architecture leadership, and knowledge sharing across Data and Analytics Engineering

HOW YOU'LL RAMP

By Day 30:

Get deep on Smartcat's current data architecture, the greenfield cloud platform build, and the transition plan from batch to streaming

Meet your partners across Data, Product, Engineering, GTM, and AI teams to understand current pain points and priorities

Get hands-on with the core stack (Databricks, dbt, Airflow, Python, SQL) and current BI tooling (e.g., Omni)

By Day 60:

Contribute to at least one concrete piece of the Next Generation Data Platform build (pipeline, streaming component, or data product)

Have identified and started applying at least one AI-powered workflow to accelerate your own engineering work (testing, documentation, debugging)

Begin shaping a semantic layer, metadata structure, or data product aimed at AI-agent or business-user consumption

By Day 90:

Own a defined piece of architecture or data product end-to-end, with measurable improvements in reliability, observability, or time-to-insight

Be actively mentoring or raising the engineering bar through code reviews and architecture input

Have a documented example of measurable productivity gains from AI adoption in your own workflow

WHAT YOU'VE ACCOMPLISHED

6+ years of experience in Data Engineering, Analytics Engineering, or a related field

A proven track record designing and operating modern cloud data platforms, ideally in high-growth SaaS environments

Experience working effectively with both technical and business stakeholders

Demonstrated ability to lead complex projects from design through delivery

Strong hands-on experience with Databricks, dbt, Airflow, Python for data engineering, SQL and data modeling, data warehousing architectures, data quality frameworks, data governance, and data orchestration/integration

Experience with streaming architectures and event-driven systems, business intelligence platforms, product analytics platforms, and CRM/customer data platforms

A demonstrated AI-first mindset — AI-powered development workflows, automation of repetitive engineering tasks, use of AI for debugging/testing/architecture exploration/documentation, and evidence of measurable productivity gains through AI adoption

Bonus: experience building AI-native data products, semantic layers, RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational business data