Collecting, transforming, and sending data through the chain in the proper format up to the Warehouse Level.
Understand the data challenges experienced by the organization, then contribute to the proposal and development of data solutions they can put in place by way of ETL processes
Facilitate overall data availability, consumption and awareness of key business data points and metrics across the organization. In practice, this means making data more easily accessible in real time, allowing non-technical people to simply view and interact with data visualization and reports.
Build, deploy, maintain and orchestrate ETL processes using a variety of different data analytics and data engineering tools.
Work with project managers and senior business stakeholders to understand the business problem and landscape in which they need to operate.
During my tenure as an ETL developer in Informatica, I was responsible for designing and implementing complex data extraction, transformation, and loading processes. I collaborated with cross-functional teams to gather and analyze business requirements, and then created data integration solutions to meet those needs. This involved developing and maintaining ETL workflows, optimizing performance, and ensuring data quality and accuracy. I also had the opportunity to work on various Informatica PowerCenter features, such as PowerExchange, PowerCenter Repository, and PowerCenter Designer, to streamline data integration processes and enhance overall data management efficiency.
As an associate at Cognizant, I had a pivotal role in loading and maintaining transactional data, with a specific focus on managing CDC (Change Data Capture) changes using SCD (Slowly Changing Dimension) Type-1 and Type-2 methodologies. I was also involved in implementing incremental data loading strategies through tools such as Syncsort DMX-h, which allowed for efficient extraction and processing of only the changed data. This approach ensured that the transactional data was kept up-to-date and aligned with business needs, making use of industry-standard practices for data management.