Valleysoft, a reputable IT services company, is seeking a skilled Data Engineer to join our dynamic team. The successful candidate will be responsible for designing, developing, and maintaining scalable data solutions to support business analytics and decision-making processes. Key Responsibilities: Design, build, and optimize data pipelines and ETL processes for collecting, transforming, and storing data across multiple sources. Develop and maintain data warehouses, data marts, and data lakes to enable efficient data access and analysis. Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver relevant solutions. Ensure data quality, consistency, and security throughout the data lifecycle. Monitor and troubleshoot data pipeline performance and implement improvements. Keep up-to-date with emerging data engineering technologies and best practices. Private Health Insurance Training & Development Build and maintain data warehouse and data marts, including ODS and internal departmental marts. Gather, process, maintain, and analyze large datasets, ensuring data accuracy and quality. Design, automate, and support ETL pipelines and data flows across multiple applications. Create logical and physical data flow models and mapping designs for ETL processes. Strong SQL skills (performance tuning, index management) and solid understanding of database structures. Strong understanding of data modeling concepts. Familiarity with SQL and NoSQL storage, and Big Data technologies (Hive, Impala, YARN, Kafka, etc.). Informatica-specific requirements: Hands-on experience with Informatica PowerCenter development (mappings, sessions, workflows). Strong knowledge of Informatica transformations (e.g., Lookup, Joiner, Aggregator, Expression, Router, Sorter) and performance tuning. Experience with Informatica Developer. Experience with Informatica B2B is a plus. Ability to design, automate, monitor, and troubleshoot ETL jobs, including scheduling. Banking sector requirements (preferred): Previous experience in the banking sector, preferably business banking, including working with banking datasets. Strong understanding of data quality and reconciliation expectations in banking environments. Experience working under regulated environments and change control practices is a strong plus.