data engineer for AI-native data products

Smartcat is building an AI-powered platform for multilingual content creation, translation, and localization at scale. Its platform combines generative AI, human-in-the-loop workflows, and an Enterprise Skill Graph to help enterprises turn knowledge into action and improve productivity.

• Evolve the data architecture into a scalable, cloud-native, AI-first platform, transitioning from batch-oriented pipelines to near-real-time and streaming systems;
• Improve reliability, observability, governance, and performance across the data stack while establishing engineering standards and best practices;
• Build data products consumable by AI agents, analytics systems, and business users, including semantic layers and metadata management;
• Use AI to accelerate development, testing, documentation, monitoring, and operational workflows;
• Design systems that allow AI agents to safely query, understand, and act on business data;
• Improve business intelligence and data accessibility for non-technical stakeholders while maintaining governance and quality;
• Raise engineering standards through mentorship, code reviews, architecture leadership, and knowledge sharing across Data and Analytics Engineering;
• Learn Smartcat’s current data architecture, greenfield cloud platform, and transition plan from batch to streaming;
• Collaborate with Data, Product, Engineering, GTM, and AI teams to understand pain points and priorities;
• Work hands-on with Databricks, dbt, Airflow, Python, SQL, and BI tooling such as Omni;
• Contribute to the Next Generation Data Platform through a pipeline, streaming component, or data product;
• Identify and apply AI-powered workflows to accelerate engineering work;
• Shape a semantic layer, metadata structure, or data product for AI-agent or business-user consumption;
• Own a defined piece of architecture or a data product end-to-end with measurable improvements in reliability, observability, or time-to-insight;
• Mentor engineers and raise engineering standards through code reviews and architecture input;
• Document measurable productivity gains from AI adoption in the engineering workflow.

• 6+ Years of experience in Data Engineering, Analytics Engineering, or a related field;
• Proven experience designing and operating modern cloud data platforms, ideally in high-growth SaaS environments;
• Experience working effectively with technical and business stakeholders;
• 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;
• Experience with data warehousing architectures, data quality frameworks, data governance, and data orchestration or integration;
• Experience with streaming architectures, event-driven systems, business intelligence platforms, product analytics platforms, and CRM or customer data platforms;
• AI-first mindset with experience using AI for development workflows, automation, debugging, testing, architecture exploration, or documentation;
• Evidence of measurable productivity gains through AI adoption;
• Nice to have: Experience building AI-native data products, semantic layers, RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational business data.

• Smartcat has a global team of 200+ people across 30+ countries;
• Most roles are remote-friendly;
• Smartcat operates through a hub-and-spoke model anchored in New York, London, Lisbon, Costa Rica, Serbia, Armenia, Georgia, and Spain;
• The company provides an inclusive environment focused on respect, appreciation, results, and engagement.