Data Engineer-Data Integration

Job Description

As Data Engineer at IBM you will harness the power of data to unveil captivating stories and intricate patterns. You’ll contribute to data gathering, storage, and both batch and real-time processing. Collaborating closely with diverse teams, you’ll play an important role in deciding the most suitable data management systems and identifying the crucial data required for insightful analysis. As a Data Engineer, you’ll tackle obstacles related to database integration and untangle complex, unstructured data sets. In this role, your responsibilities may include: Implementing and validating predictive models as well as creating and maintain statistical models with a focus on big data, incorporating a variety of statistical and machine learning techniques Designing and implementing various enterprise search applications such as Elasticsearch and Splunk for client requirements Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviour’s. Build teams or writing programs to cleanse and integrate data in an efficient and reusable manner, developing predictive or prescriptive models, and evaluating modelling results Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Expertise in designing and implementing scalable data warehouse solutions on Snowflake, including schema design, performance tuning, and query optimization. Strong experience in building data ingestion and transformation pipelines using Talend to process structured and unstructured data from various sources. Proficiency in integrating data from cloud platforms into Snowflake using Talend and native Snowflake capabilities. Hands-on experience with dimensional and relational data modelling techniques to support analytics and reporting requirements Preferred technical and professional experience Understanding of optimizing Snowflake workloads, including clustering keys, caching strategies, and query profiling. Ability to implement robust data validation, cleansing, and governance frameworks within ETL processes. Proficiency in SQL and/or Shell scripting for custom transformations and automation tasks