Chief Data Engineering Specialist

2 weeks ago

Palakkad, Kerala, India beBeeData Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
Key Senior Data Engineer Role

As a seasoned data engineer, you will lead the development of scalable, high-performance data pipelines using Snowflake and dbt. Your role will involve defining architectural best practices and driving data transformation at scale.

  • Design and implement modular, test-driven ELT pipelines using dbt on Snowflake.
  • Create layered data models (e.g., staging, intermediate, mart layers / medallion architecture) 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 using dbt docs.
  • 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.
  • Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets.
  • Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards.

To succeed in this role, you should have 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.

The ideal candidate will possess expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management. Additionally, they will have experience in dbt development: modular model design, macros, tests, documentation, and version control using Git.

Proficiency in orchestrating workflows using dbt Cloud, Airflow, or Azure Data Factory is also essential. The candidate should be comfortable working with data ingestion from cloud storage and APIs.

A strong understanding of modern data warehousing principles, dimensional modelling, and slow changing dimensions is required. Experience implementing Medallion Architecture and working with Parquet, JSON, CSV, or other data formats is a plus.

The selected candidate will thrive in client-facing roles with competing/changing priorities and fast-paced delivery cycles. They should possess excellent stakeholder communication skills, project ownership, mentorship, and agile practices.

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