Senior Data Platform Specialist

1 week ago

Delhi, Delhi, India beBeeDataEngineer Full time ₹ 1,00,00,000 - ₹ 1,50,00,000
About the Role">

We are seeking a skilled Data Engineer to contribute to our cutting-edge data platform built on a medallion data lake architecture.

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Key Responsibilities :

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  • Design, build, and optimize scalable data pipelines using Airflow, dbt, and SQL to process and transform data in our medallion architecture (bronze, silver, gold layers).
  • Manage and maintain data storage solutions using Snowflake and AWS S3, ensuring performance, reliability, and cost efficiency.
  • Implement infrastructure as code using Terraform to provision and manage AWS resources such as Lambda, S3, and other cloud services.
  • Work with Delta tables and Apache Spark to handle large-scale data processing and ensure data quality and consistency.
  • Collaborate with AI/ML teams to integrate data pipelines with AI engineering workflows, supporting common LLM patterns (e.g., RAG, fine-tuning, prompt engineering) and architectures.
  • Monitor and troubleshoot data pipelines, ensuring high availability, data integrity, and efficient performance.
  • Contribute to the evolution of our data architecture, incorporating best practices for scalability, security, and governance.
  • Stay updated on industry trends and emerging technologies in data engineering and AI to drive continuous improvement.
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Required Skills and Qualifications :

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  • Proficiency in SQL and experience with Snowflake for data warehousing and querying.
  • Hands-on experience with Airflow for workflow orchestration and dbt for data transformation.
  • Strong knowledge of AWS services (e.g., S3, Lambda) and infrastructure management using Terraform.
  • Familiarity with Delta tables and Apache Spark for large-scale data processing.
  • Familiarity with medallion data lake architecture and best practices for data modeling and pipeline design.
  • Basic understanding of AI engineering concepts, including embeddings, vector stores, common LLM patterns (e.g., retrieval-augmented generation, model fine-tuning) and architectures.
  • Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
  • Excellent communication skills to partner with cross-functional teams, including data scientists and AI engineers.
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