Junior Data Engineer

RequiredExperience 1–3 years of experience in Data Engineering, ETL/ELT development, or Data Warehousing. Hands-on experience with IBM DataStage, SQL, Python, Snowflake, and dbt. Basic understanding of data warehousing concepts, dimensional modeling, and ETL/ELT processes. Experience developing and maintaining data pipelines and transformation workflows. Familiarity with cloud-based data platforms and modern data engineering practices. Exposure to reporting, analytics, and Business Intelligence tools is an advantage. Strong analytical, problem-solving, and communication skills. Key Responsibilities Develop, maintain, and optimize ETL/ELT pipelines using DataStage, Python, Snowflake, and dbt. Design and implement data transformation workflows to support reporting and analytics. Collaborate with business stakeholders to understand data requirements and translate them into technical solutions. Analyze business and technical requirements to support data integration and reporting needs. Write, optimize, and troubleshoot SQL queries for data extraction, transformation, and validation. Build and maintain scalable data pipelines that ensure data quality, consistency, and reliability. Perform data profiling, validation, and reconciliation to ensure data integrity. Support the development and maintenance of data models and data warehouse structures. Assist in identifying and resolving data quality issues and production incidents. Work closely with Data Analysts, BI Developers, and other engineering teams to deliver high-quality data solutions. Support dashboard and reporting initiatives by providing clean, reliable datasets. Create and maintain technical documentation, ETL mappings, and data flow diagrams. Participate in code reviews, testing, deployment, and continuous improvement activities. Contribute to process automation and performance optimization initiatives. Must-Have Technical Skills IBM DataStage SQL (query writing and optimization) Python Snowflake dbt (Data Build Tool) ETL/ELT Development Data Warehousing Concepts Data Modeling Git or Version Control (preferred) Basic understanding of cloud data platforms