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описание
Smartcat provides an AI-powered platform that enables enterprise teams to create, translate, and localize global content at scale. Its platform combines generative AI, human-in-the-loop workflows, and an Enterprise Skill Graph to turn knowledge into action and scale.
задачи
Evolve the data architecture into a scalable, cloud-native, AI-first platform, transitioning batch-oriented pipelines to near real-time and streaming systems
Improve reliability, observability, governance, and performance across the data stack, and establish engineering standards and best practices
Build AI-ready data products for 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 let AI agents safely query, understand, and act on business data
Improve business intelligence and data accessibility, enabling self-service analytics for non-technical stakeholders without sacrificing governance or quality
Mentor colleagues and raise engineering standards through code reviews, architecture leadership, and knowledge sharing across Data and Analytics Engineering
Learn the current data architecture, greenfield cloud platform build, and transition plan from batch to streaming
Meet partners across Data, Product, Engineering, GTM, and AI teams to understand pain points and priorities
Contribute to the Next Generation Data Platform build through a pipeline, streaming component, or data product
Identify and apply an AI-powered workflow 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, improving reliability, observability, or time-to-insight
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 track record designing and operating modern cloud data platforms
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, data modeling, data warehousing architectures, data quality frameworks, data governance, and data orchestration and integration
Experience with streaming architectures, event-driven systems, business intelligence platforms, product analytics platforms, and CRM/customer data platforms
AI-first mindset, including AI-powered development workflows, automation of repetitive engineering tasks, and using AI for debugging, testing, architecture exploration, and documentation; evidence of measurable productivity gains through AI adoption
Будет плюсом: experience building AI-native data products, semantic layers, RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational business data
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