Инженер данных

Smartcat provides an AI-powered platform that enables enterprises to create, translate, and localize global content at scale. The company develops agentic AI solutions, combining generative AI with human-in-the-loop workflows to build hybrid workforces of human employees and AI agents.

• Evolve the architecture into a next-generation data platform by building scalable cloud-native AI-first architecture;
• Lead the transition from batch-oriented pipelines to near real-time and streaming data systems;
• Improve reliability, observability, governance, and performance across the data stack;
• Establish engineering standards and best practices for data development;
• Build data products consumable by AI agents, analytics systems, and business users;
• Enable semantic layers, metadata management, and knowledge structures to make data actionable;
• Create foundations for agent-driven reporting, forecasting, and business intelligence;
• Transform business metrics from static dashboards into living operational systems;
• Use AI to accelerate development, testing, documentation, monitoring, and operational workflows;
• Design systems that allow AI agents to query, understand, and act on business data safely;
• Evaluate emerging AI technologies to increase productivity across the data organization;
• Make analytics simpler and more accessible for non-technical stakeholders;
• Improve usability and adoption of BI tools;
• Enable self-service analytics while maintaining governance and data quality;
• Reduce time-to-insight across Product, Revenue, Marketing, Customer Success, and Finance teams;
• Raise the engineering bar through mentorship, code reviews, architecture leadership, and knowledge sharing;
• Influence technical direction across Data Engineering and Analytics Engineering;
• Partner with stakeholders to align platform investments with business priorities.

• 6+ Years of experience in Data Engineering, Analytics Engineering, or a related field;
• Proven track record designing and operating modern cloud data platforms;
• Experience in high-growth SaaS environments;
• Experience working 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;
• Expertise in data warehousing architectures, data quality frameworks, data governance, and data orchestration;
• Experience with streaming architectures, event-driven systems, BI platforms, product analytics, CRM, and customer data platforms;
• Ability to demonstrate AI-powered development workflows, automation of repetitive tasks, and use of AI for debugging, testing, and architecture exploration;
• Nice to have: Experience building AI-native data products, semantic layers, RAG systems, vector databases, or knowledge graphs, and experience enabling AI agents to consume operational business data.

• No conditions specified