Chief Data Pipeline Architect

4 days ago

Nashik, Maharashtra, India beBeeData Full time ₹ 15,00,000 - ₹ 25,00,000

Key Data Pipelines Expertise

To succeed in this challenging role, you will need to have a deep understanding of designing and developing scalable data pipelines that power business insights.

Main Responsibilities:

  • Develop ETL/ELT pipelines using Snowflake Tasks, Streams, and Stored Procedures to ensure seamless data integration.
  • Create optimized data models with clustering keys and micro-partitioning strategies to enhance data storage efficiency.
  • Implement real-time data ingestion using Snowpipe and integrate external tables for robust data lake management.
  • Develop complex JavaScript/SQL stored procedures and UDFs to facilitate data transformations and analyses.
  • Write high-performance SQL queries leveraging window functions, CTEs, and pivots to extract valuable insights from large datasets.
  • Manage multi-cluster warehouses and implement resource monitors to optimize cost control and performance.

Required Skills and Qualifications:

  • Familiarity with Snowflake development, including strong Snowflake SQL and basic JavaScript skills.
  • Working knowledge of Snowflake Tasks, Streams, Stages, File Formats, Time Travel, and dbt core concepts (models, sources, tests, snapshots).
  • Understanding of warehouse sizing, auto-suspend/resume, and query optimization techniques.
  • Familiarity with RBAC, data types, semi-structured data (JSON/XML), and basic knowledge of SnowSQL CLI and Snowflake web interface.

Business Requirements:

This role requires a strong background in data engineering, with a focus on designing and implementing scalable data pipelines. If you have experience working with Snowflake and are passionate about extracting insights from large datasets, we encourage you to apply.

Your Key Responsibilities Will Include:

  • Designing and implementing data pipelines to meet business requirements.
  • Developing and maintaining data models to ensure efficient data storage.
  • Implementing real-time data ingestion and managing data lakes.
  • Developing complex stored procedures and UDFs to support data transformations.
  • Writing high-performance SQL queries to extract insights from large datasets.
  • Managing multi-cluster warehouses and optimizing cost control and performance.

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