Chief Data Architect

1 week ago

Thrissur, Kerala, India beBeeDataEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Senior Data Engineer Job Summary

We are seeking a highly skilled and experienced senior data engineer to lead the design and implementation of scalable, high-performance data pipelines.

In this role, you will define architectural best practices, drive data transformation at scale, work closely with clients to translate business needs into robust data solutions, and mentor junior engineers.

Key Responsibilities:

  • Architect and implement modular, test-driven ELT pipelines using dbt on Snowflake.
  • Design layered data models aligned with dbt best practices.
  • Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage, and databases into Snowflake.
  • Optimize Snowflake for performance and cost: warehouse sizing, clustering, materializations, query profiling, and credit monitoring.
  • Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation.
  • Orchestrate workflows using dbt Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines.
  • Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies.

Requirements:

  • Bachelor's or master's degree in computer science, data engineering, or a related field.
  • Certifications such as Snowflake SnowPro Advanced, dbt Certified Developer are a plus.

Experience:

  • 5 to 8 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production environments.

Technical Skills:

  1. Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management.
  2. Experience in dbt development: modular model design, macros, tests, documentation, and version control using Git.

Soft Skills & Leadership:

  • Ability to thrive in client-facing roles with competing/changing priorities and fast-paced delivery cycles.
  • Stakeholder Communication: Collaborate with business stakeholders to understand objectives and convert them into actionable data engineering designs.
  • Project Ownership: End-to-end delivery including design, implementation, and monitoring.
  • Mentorship: Guide junior engineers and establish best practices; build new skills in the team.

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