N Consulting Global’s Post

ETL engineer Onsite in Katy, TX and in the office daily. Houston TX might be a possibility.   Role Overview & Required Skills: Targeting candidates focused on Data Acquisition and Data Integration using DataStage. The contractor should have deep experience working with flat files, XML transformations, as well as ETL development using DataStage V8 or greater. They need to have solid experience with Quality Stage Address Standardization CASS modules. The contractor needs experience integrating the components of ORACLE, SQL, and DataStage (ETL). Excellent communication skills are a must.   Responsibilities: 1. Data Extraction: Extract data from various sources such as databases, files, APIs, and web services using ETL tools or programming languages. 2. Data Transformation: Cleanse, validate, and transform the extracted data to ensure its accuracy, consistency, and integrity. This may involve data mapping, data conversion, data aggregation, and data enrichment. 3. Data Loading: Load the transformed data into the target systems such as data warehouses, data marts, or operational databases. This includes defining data structures, creating tables, and optimizing data loading processes. 4. ETL Process Development: Design, develop, and maintain ETL processes and workflows using ETL tools (e.g., Informatica, Talend, SSIS) or programming languages (e.g., Python, SQL). This involves writing efficient and scalable code to handle large volumes of data. 5. Data Quality Assurance: Perform data quality checks and implement data validation rules to ensure the accuracy, completeness, and consistency of the data. Identify and resolve data quality issues or anomalies. 6. Performance Optimization: Optimize ETL processes for improved performance, scalability, and efficiency. Identify and resolve performance bottlenecks, optimize data transformations, and fine-tune data loading processes. 7. Documentation and Reporting: Document ETL processes, data mappings, and data lineage for future reference. Generate reports and provide insights on data quality, data lineage, and ETL process performance. 8. Collaboration and Communication: Collaborate with cross-functional teams including data analysts, data engineers, business analysts, and stakeholders to understand data requirements, gather feedback, and ensure successful ETL implementation.   Requirements:

Day26of100 #datastage #datastagedeveloper #movingtowardsdataengineering #ARCHITECTURE_OF_DATASTAGE : #Client_Tier: Users interact with DataStage through client applications, such as the DataStage Designer, Director, and Administrator clients. The Designer is used for designing and developing data integration jobs. The Director is used for managing and monitoring job execution. The Administrator client is used for configuring and managing DataStage projects, users, and resources. #Server_Tier: The server tier consists of DataStage engine components responsible for executing data integration jobs. It includes the DataStage engine, which orchestrates job execution, handles metadata operations, and manages job repositories. The engine supports parallel processing and can distribute job execution across multiple processing nodes for scalability and performance. #Repository_Database: DataStage stores metadata, job definitions, and configuration information in a repository database. The repository database serves as a central repository for storing and managing metadata related to DataStage jobs, stages, parameters, and job schedules. It enables version control, lineage tracking, and collaboration among multiple users working on DataStage projects. #DataStage_Components: DataStage jobs are composed of various components, including stages, links, containers, and parameters. Stages represent individual processing units within a job and perform specific data integration tasks, such as reading data from sources, transforming data, and loading data into targets. Links define the flow of data between stages, specifying the sequence and direction of data movement within the job. Containers are reusable components that encapsulate groups of stages and logic within a job, promoting modularity and reusability. Parameters allow users to define and manage configurable properties within jobs, facilitating flexibility and customization. #Parallel_Processing: DataStage employs a parallel processing architecture to optimize job execution and maximize hardware utilization. Parallelism allows jobs to be divided into smaller, parallel tasks that can be executed concurrently across multiple processing nodes. DataStage dynamically allocates resources and workload distribution, ensuring efficient utilization of computing resources and minimizing job execution times. #Data_Integration_Connectivity: DataStage provides connectivity to a wide range of data sources and targets, including databases, flat files, cloud platforms, enterprise applications, and big data systems. It offers built-in connectors, adapters, and drivers to facilitate seamless data integration across heterogeneous environments. DataStage supports both batch and real-time data integration scenarios, enabling organizations to process data in near real-time and meet diverse business requirements. Follow karthikeyan udayakumar #learningeveryday #learningandgrowing