Senior AI-First Data Engineer

Sobre el puesto

About Smartcat

Smartcat is building the future of work, where human expertise meets digital teammates to drive 10x to 1000x productivity gains for the world’s leading enterprises.

We’re on the frontier of an entirely new category: Agentic AI. We enable enterprises to build high-performing hybrid workforces made up of both humans and AI agents. These AI agents aren’t generic copilots. They’re fully trained digital teammates that learn from your best people, your content, and your business strategy—ready to get to work from day one.

Our platform combines generative AI, human-in-the-loop workflows, and a living Enterprise Skill Graph that continuously learns and improves. Whether you're launching a product globally, onboarding new hires, translating learning content, or aligning legal teams across regions, Smartcat turns knowledge into action and action into scale.

Over 1,000 companies, including 20% of the Fortune 500, rely on Smartcat to bring their business to the world—instantly, accurately, and in every language. As a Series C company with consistently high year-over-year growth, we’re scaling fast and investing in people who want to shape the future of work with us.

Join us in unlocking global potential, one human and agent team at a time.

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

Why joining Smartcat might be your best move so far

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A global, connected team We’re a team of 200+ people across 30+ countries, working together through a hub-and-spoke model anchored in eight key locations: New York, London, Lisbon, Costa Rica, Serbia, Armenia, Georgia, and Spain. Most of our roles are remote-friendly, giving you the flexibility to do your best work from where you thrive. For customer-facing teams, we prioritize proximity to our customers, ensuring we stay…