Data Engineer Lead
7 days ago
Salary: Upto 70 Mil
Job Description
· Lead the full architecture and development of a modern, cloud-native data platform on Snowflake, aligned with Lakehouse principles.
· Build and maintain advanced ETL/ELT pipelines with strong reliability, performance, scalability, and reusability.
· Design and optimize analytical data models (star schema, normalized models) for BI, analytics, and machine learning use cases.
· Partner closely with data scientists and AI engineers to deliver training datasets, model-scoring pipelines, and data services supporting AI/agent applications.
· Manage ingestion and integration of both structured and unstructured data while ensuring robust data governance, security, and compliance.
· Promote best practices in code quality, documentation, testing, and CI/CD across the data engineering team.
· Mentor junior and mid-level engineers, perform code reviews, and cultivate a high-standards engineering culture.
· Collaborate directly with global product and business teams in fluent English—covering requirement gathering, solution design, and presenting architectural recommendations.
· Support the development of AI agents and intelligent data services leveraging real-time data and vector-based retrieval.
· Lead performance optimization, cost control, and scalable infrastructure management across the data ecosystem.
Required working experience
· 5+ years of hands-on experience in data engineering or large-scale data platform development.
Required Languages:
· Excellent English communication skills—comfortable working with international teams and senior stakeholders.
Required knowledge and skills
· Demonstrated expertise with Snowflake, including architecture, performance tuning, cost optimization, and ELT design. (Experience with Databricks/Spark/Delta Lake is a plus.)
· Advanced proficiency in Python and SQL with the ability to deliver production-quality code.
· Practical experience building ETL/ELT workflows using Airflow, dbt, or similar orchestration frameworks.
· Strong knowledge of Lakehouse architecture, data warehousing concepts, and large-scale performance optimization.
· Solid understanding of cloud platforms (AWS, Azure, or GCP).
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