О компании
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.
Задачи
•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
Требования
•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.
Условия
•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
