Job Description

Job Summary

We are looking for an experienced Data Engineer with 7+ years of expertise in building scalable data pipelines and modern data platforms. The ideal candidate will have strong hands-on experience in PySpark, SQL, Python, Airflow, dbt, and Snowflake, with the ability to design robust data architectures that support analytics and business intelligence needs.

Responsibilities

Design, develop, and maintain scalable and efficient data pipelines using PySpark and Python
Build and optimize data workflows and orchestration pipelines using Apache Airflow
Develop and manage data transformation models using dbt (Data Build Tool)
Implement and maintain data warehouse solutions on Snowflake
Write complex and optimized SQL queries for data extraction, transformation, and analysis
Ensure data quality, integrity, and reliability across pipelines and systems
Collaborate with Data Scientists, Analysts, and Business teams to understand data requirements and deliver solutions
Optimize data processing performance and cost efficiency in cloud environments
Implement best practices for data governance, security, and compliance
Troubleshoot and resolve data-related issues in production environments
Mentor junior engineers and contribute to improving engineering standards and practices

Required Skills

7+ years of experience in Data Engineering or related roles
Must have strong experience in DBT
Strong proficiency in Python and PySpark for large-scale data processing
Advanced knowledge of SQL for data manipulation and performance tuning
Hands-on experience with Apache Airflow for workflow orchestration
Strong experience with dbt for data transformation and modeling
Expertise in Snowflake including data modeling, performance tuning, and cost optimization
Experience working with cloud platforms (AWS, Azure, or GCP)
Strong understanding of data warehousing concepts and ETL/ELT frameworks
Familiarity with version control systems (e.g., Git) and CI/CD pipelines
Excellent problem-solving and analytical skills

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