Job Title

Job SummaryWe 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.ResponsibilitiesDesign, develop, and maintain scalable and efficient data pipelines using PySpark and PythonBuild and optimize data workflows and orchestration pipelines using Apache AirflowDevelop and manage data transformation models using dbt (Data Build Tool)Implement and maintain data warehouse solutions on SnowflakeWrite complex and optimized SQL queries for data extraction, transformation, and analysisEnsure data quality, integrity, and reliability across pipelines and systemsCollaborate with Data Scientists, Analysts, and Business teams to understand data requirements and deliver solutionsOptimize data processing performance and cost efficiency in cloud environmentsImplement best practices for data governance, security, and complianceTroubleshoot and resolve data-related issues in production environmentsMentor junior engineers and contribute to improving engineering standards and practicesRequired Skills7+ years of experience in Data Engineering or related rolesStrong proficiency in Python and PySpark for large-scale data processingAdvanced knowledge of SQL for data manipulation and performance tuningHands-on experience with Apache Airflow for workflow orchestrationStrong experience with dbt for data transformation and modelingExpertise in Snowflake including data modeling, performance tuning, and cost optimizationExperience working with cloud platforms (AWS, Azure, or GCP)Strong understanding of data warehousing concepts and ETL/ELT frameworksFamiliarity with version control systems (e.g., Git) and CI/CD pipelinesExcellent problem-solving and analytical skills