Application Architect

Role Overview: As an Application Architect, you will provide functional and technical expertise to plan, analyze, define, and support the delivery of future functional and technical capabilities for an application or group of applications. Your typical day will involve collaborating with various teams to assess impacts, producing estimates for client work requests, and ensuring that the applications meet the evolving needs of the organization. You will engage in discussions to identify potential improvements and innovations, while also guiding the team in implementing best practices and methodologies to enhance application performance and user experience. Key Responsibilities: - Work as part of the data engineering team to build, maintain, and optimize scalable data pipelines for large-scale data processing. - Develop and implement ETL/ELT processes using PySpark, Spark, and other relevant tools to move and transform data from various sources. - Assist in designing and deploying solutions in major cloud platforms such as AWS, Azure, or GCP. - Support the development and maintenance of Big Data processing frameworks and data lakes to handle structured and unstructured data. - Collaborate with data scientists, analysts, and other engineers to ensure data accuracy and availability. - Implement data ingestion strategies, ensuring the secure and efficient movement of data across different storage solutions. - Work on real-time streaming data pipelines and batch data processing to handle high-volume workloads. - Develop and maintain reusable code for data extraction, transformation, and loading (ETL) operations. - Contribute to performance tuning of Spark jobs and data pipelines to ensure scalability and efficiency. - Assist in maintaining governance and data security practices across cloud platforms. Qualification Required: - Experience with AWS, Azure, or GCP for data engineering workflows. - Strong proficiency in PySpark, Spark, or similar frameworks for building scalable data pipelines. - Understanding of Big Data architectures, data storage, and data processing concepts. - Familiarity with cloud-native data storage solutions such as S3, Blob Storage, BigQuery, or Redshift. - Experience with data orchestration tools like Apache Airflow or similar. - Knowledge of data formats like Parquet, Avro, or JSON. - Strong coding skills in Python for building data pipelines. - Good understanding of SQL and database technologies. - Excellent troubleshooting, debugging, and performance optimization skills. (Note: Additional details of the company were not provided in the job description.) Role Overview: As an Application Architect, you will provide functional and technical expertise to plan, analyze, define, and support the delivery of future functional and technical capabilities for an application or group of applications. Your typical day will involve collaborating with various teams to assess impacts, producing estimates for client work requests, and ensuring that the applications meet the evolving needs of the organization. You will engage in discussions to identify potential improvements and innovations, while also guiding the team in implementing best practices and methodologies to enhance application performance and user experience. Key Responsibilities: - Work as part of the data engineering team to build, maintain, and optimize scalable data pipelines for large-scale data processing. - Develop and implement ETL/ELT processes using PySpark, Spark, and other relevant tools to move and transform data from various sources. - Assist in designing and deploying solutions in major cloud platforms such as AWS, Azure, or GCP. - Support the development and maintenance of Big Data processing frameworks and data lakes to handle structured and unstructured data. - Collaborate with data scientists, analysts, and other engineers to ensure data accuracy and availability. - Implement data ingestion strategies, ensuring the secure and efficient movement of data across different storage solutions. - Work on real-time streaming data pipelines and batch data processing to handle high-volume workloads. - Develop and maintain reusable code for data extraction, transformation, and loading (ETL) operations. - Contribute to performance tuning of Spark jobs and data pipelines to ensure scalability and efficiency. - Assist in maintaining governance and data security practices across cloud platforms. Qualification Required: - Experience with AWS, Azure, or GCP for data engineering workflows. - Strong proficiency in PySpark, Spark, or similar frameworks for building scalable data pipelines. - Understanding of Big Data architectures, data storage, and data processing concepts. - Familiarity with cloud-native data storage solutions such as S3, Blob Storage, BigQuery, or Redshift. - Experience with data orchestration tools like Apache Airflow or similar. - Knowledge of data formats like Parquet, Avro, or JSON. - Strong coding skills in Pytho