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About the role
Job Description: The ideal candidate will have robust expertise in Snowflake , Hadoop ecosystem , PySpark , and SQL , and will play a key role in enabling data-driven decision-making across the organization. Responsibilities: Design, develop, and optimize robust data pipelines using PySpark and SQL. Implement and manage data warehousing solutions using Snowflake. Work with large-scale data processing frameworks within the Hadoop ecosystem. Collaborate with data scientists, analysts, and business stakeholders to understand data requirements. Ensure data quality, integrity, and governance across all data platforms. Monitor and troubleshoot data pipeline performance and reliability. Automate data workflows and implement best practices for data engineering. Qualifications: 5+ years of experience in data engineering or related roles. Robust hands-on experience with Snowflake including data modeling, performance tuning, and security. Knowledge in PySpark for distributed data processing. Solid understanding of Hadoop ecosystem (HDFS, Hive, Spark, etc.). Advanced SQL skills for data manipulation and analysis. Experience with ETL tools and orchestration frameworks (e.g., Airflow, DBT). Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus. Excellent problem-solving and communication skills. Job Description: The ideal candidate will have robust expertise in Snowflake , Hadoop ecosystem , PySpark , and SQL , and will play a key role in enabling data-driven decision-making across the organization. Responsibilities: Design, develop, and optimize robust data pipelines using PySpark and SQL. Implement and manage data warehousing solutions using Snowflake. Work with large-scale data processing frameworks within the Hadoop ecosystem. Collaborate with data scientists, analysts, and business stakeholders to understand data requirements. Ensure data quality, integrity, and governance across all data platforms. Monitor and troubleshoot data pipeline performance and reliability. Automate data workflows and implement best practices for data engineering. Qualifications: 5+ years of experience in data engineering or related roles. Robust hands-on experience with Snowflake including data modeling, performance tuning, and security. Knowledge in PySpark for distributed data processing. Solid understanding of Hadoop ecosystem (HDFS, Hive, Spark, etc.). Advanced SQL skills for data manipulation and analysis. Experience with ETL tools and orchestration frameworks (e.g., Airflow, DBT). Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus. Excellent problem-solving and communication skills.
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