Lead Data Engineer

We are looking for a Lead Data Engineer with excellent English communication skills to take ownership of our data platform architecture and lead a team of engineers in building nextgeneration, AI-powered data solutions. This role demands deep expertise in Databricks or Snowflake, ETL pipeline design, and Lakehouse architecture, as well as experience supporting AI/ML initiatives and AI agents.

Responsibilities

• Lead the end-to-end architecture and development of a modern, cloud-native data platform using Snowflake, following Lakehouse principles.

• Own and implement advanced ETL/ELT pipelines, ensuring high reliability, performance, scalability, and reusability.

• Design and optimize data models (star schema, normalized models) for analytics, BI, and ML workloads.

• Collaborate closely with data scientists and AI engineers to deliver training data pipelines, model scoring layers, and agent-ready data services.

• Oversee the integration of structured and unstructured data sources, ensuring strong data governance, security, and compliance.

• Champion code quality, documentation, testing, and CI/CD best practices across the data engineering team.

• Mentor junior and mid-level engineers, conduct code reviews, and foster a culture of technical excellence.

• Work directly with global product and business stakeholders using fluent English, including requirement gathering, translating needs into scalable solutions, and presenting architectural recommendations.

• Contribute to the development of AI agents and intelligent data services, leveraging real-time data and vector-based retrieval.

• Lead performance tuning, cost optimization, and infrastructure scaling across the data stack

• 5+ years of hands-on experience in data engineering or large-scale data platform development.

• Strong, proven expertise with Snowflake: platform architecture, performance tuning, cost management, and ELT pipeline design. (Experience with Databricks/Spark/Delta Lake is an additional advantage.)

• Expert-level proficiency in Python and SQL, capable of writing efficient, production-ready code.

• Solid experience building ETL/ELT workflows using Airflow, dbt, or similar orchestration tools.

• Deep understanding of Lakehouse architecture, data warehousing concepts, and large-scale performance optimization.

• Strong working knowledge of cloud environments (AWS, Azure, or GCP).

• Excellent English communication skills (both verbal and written) – able to engage confidently with international stakeholders and senior leadership.

• Strong leadership, problem-solving, and project management abilities.

Preferred Qualifications

• Experience with AI/ML lifecycle, MLOps, or AI agent integration (vector databases, embedding models, etc.).

• Experience with real-time/streaming data pipelines (e.g., Kafka, Kinesis).

• Familiarity with AI frameworks like LangChain, OpenAI/Gemini APIs, HuggingFace, or similar LLM-based tools.

• Knowledge of data governance frameworks, including data catalogs, lineage, and privacy controls.

• Prior experience in high-growth environments, especially in AI/tech startups or global enterprises.