Lead the architecture design and end-to-end development of a modern, cloud-native data platform on Snowflake, aligned with Lakehouse architecture principles.
Design, implement, and own high-performance ETL/ELT pipelines, ensuring scalability, reliability, and reusability.
Build and optimize data models (star schema, normalized models) to support analytics, BI, and machine learning workloads.
Collaborate closely with Data Scientists and AI Engineers to deliver training data pipelines, model inference layers, and agent-ready data services.
Manage the integration of structured and unstructured data sources, ensuring robust data governance, security, and compliance standards.
Promote best practices in code quality, documentation, testing, and CI/CD across the data engineering function.
Mentor junior and mid-level engineers, perform code reviews, and foster a culture of technical excellence.
Engage directly with global product and business stakeholders in English, including requirement analysis, solution design, and architectural presentations.
Contribute to the development of AI-driven data services and intelligent agents, leveraging real-time data and vector-based retrieval.
Drive performance optimization, cost efficiency, and infrastructure scalability across the data platform.
REQUIREMENTS
5+ years of hands-on experience in data engineering or large-scale data platform development.
Proven, in-depth expertise with Snowflake (3-4 years), including platform architecture, performance tuning, cost optimization, and ELT pipeline design.
Advanced proficiency in Python and SQL, with the ability to deliver production-grade, efficient code.
Solid experience building and orchestrating ETL/ELT workflows using Airflow, dbt, or similar tools.
Strong understanding of Lakehouse architecture, data warehousing concepts, and large-scale performance optimization.
Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
Excellent English communication skills, both written and verbal, with the ability to work effectively with international stakeholders and senior leaders.
Strong leadership, analytical thinking, and project execution capabilities.
Nice to have
Experience across the AI/ML lifecycle, including MLOps or AI agent integration (vector databases, embeddings, etc.).
Hands-on experience with real-time or streaming data pipelines (Kafka, Kinesis, etc.).
Familiarity with LLM and AI frameworks such as LangChain, OpenAI/Gemini APIs, HuggingFace, or similar tools.
Knowledge of data governance frameworks, including data cataloging, lineage, and privacy management.
Background in high-growth environments, particularly AI-focused startups or global technology organizations.
