Senior Data Engineer

As a Data Engineer with 6-9 years of experience, your role will involve designing, developing, and optimizing large-scale data platforms and pipelines to support analytics and business intelligence initiatives. You should have strong hands-on experience in Python, PySpark, Snowflake, ETL development, and Big Data Architecture. Roles & Responsibilities: - Design, develop, and maintain scalable data pipelines for batch and real-time data processing. - Architect and implement robust Big Data solutions to support enterprise-wide analytics and reporting needs. - Build and optimize ETL/ELT workflows using Python, PySpark, and modern data engineering frameworks. - Develop and manage data models, data warehouses, and data marts within Snowflake environments. - Collaborate with data analysts, data scientists, and business stakeholders to understand and translate data requirements into technical solutions. - Ensure data quality, integrity, governance, and security across multiple data sources and platforms. - Optimize data processing jobs, query performance, and storage utilization to improve efficiency and reduce costs. - Monitor, troubleshoot, and resolve issues related to data ingestion, transformation, and pipeline performance. - Implement best practices for CI/CD, code reviews, testing, documentation, and deployment of data engineering solutions. - Stay current with emerging technologies and recommend improvements to enhance the organization's data architecture and engineering capabilities. Requirements: - 6-9 years of experience in Data Engineering, Data Warehousing, or Big Data technologies. - Strong programming skills in Python with experience in developing scalable data processing applications. - Hands-on expertise in PySpark for distributed data processing and transformation. - Proven experience designing and implementing Big Data architectures and data lake solutions. - Strong experience with Snowflake, including data modeling, performance tuning, and warehouse optimization. - Extensive experience building and maintaining ETL/ELT pipelines for large-scale data environments. - Good understanding of data warehousing concepts, dimensional modeling, and database design principles. - Experience working with cloud platforms such as AWS, Azure, or GCP is preferred. - Familiarity with orchestration and workflow tools such as Airflow, Azure Data Factory, or similar platforms. - Strong analytical, problem-solving, communication, and stakeholder management skills with the ability to work in an Agile environment. Preferred Qualifications: - Experience with streaming technologies such as Kafka or Spark Streaming. - Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation. - Exposure to data governance, metadata management, and data quality frameworks. - Relevant cloud or Snowflake certifications are a plus. As a Data Engineer with 6-9 years of experience, your role will involve designing, developing, and optimizing large-scale data platforms and pipelines to support analytics and business intelligence initiatives. You should have strong hands-on experience in Python, PySpark, Snowflake, ETL development, and Big Data Architecture. Roles & Responsibilities: - Design, develop, and maintain scalable data pipelines for batch and real-time data processing. - Architect and implement robust Big Data solutions to support enterprise-wide analytics and reporting needs. - Build and optimize ETL/ELT workflows using Python, PySpark, and modern data engineering frameworks. - Develop and manage data models, data warehouses, and data marts within Snowflake environments. - Collaborate with data analysts, data scientists, and business stakeholders to understand and translate data requirements into technical solutions. - Ensure data quality, integrity, governance, and security across multiple data sources and platforms. - Optimize data processing jobs, query performance, and storage utilization to improve efficiency and reduce costs. - Monitor, troubleshoot, and resolve issues related to data ingestion, transformation, and pipeline performance. - Implement best practices for CI/CD, code reviews, testing, documentation, and deployment of data engineering solutions. - Stay current with emerging technologies and recommend improvements to enhance the organization's data architecture and engineering capabilities. Requirements: - 6-9 years of experience in Data Engineering, Data Warehousing, or Big Data technologies. - Strong programming skills in Python with experience in developing scalable data processing applications. - Hands-on expertise in PySpark for distributed data processing and transformation. - Proven experience designing and implementing Big Data architectures and data lake solutions. - Strong experience with Snowflake, including data modeling, performance tuning, and warehouse optimization. - Extensive experience building and maintaining ETL/ELT pipelines for large-scale data environments.