ETL Data Engineer

In the analytics age of today, organizations look up to high-speed, scalable, and reliable data pipelines driving business intelligence and strategic decision-making. This project offers an ETL data engineering infrastructure that ingests raw feeds— from enterprise systems such as Salesforce, SAP, Oracle, and POS systems— offering inputs in Excel. Apache Spark with Scala is used to undertake transformation operations including cleaning, aggregation, and reformatting. A PostgreSQL database is the central repository, and containerization with Docker and Docker Compose enables a reproducible and standard configuration, including services of pgAdmin. Dashboards created with Power BI or Tableau provide interactive, real-time visualizations so that decision-makers can extract worthwhile insights. The system is designed to deliver high data quality, consistency, and accessibility at every level. Modularity makes it simple to expand to other sources or cloud-based resources. Leveraging Spark’s distributed computation, the pipeline is able to process high-volume workloads with enhanced speed and reduced latency. Overall, the solution demonstrates the power of data engineering tools these days to be brought together for enabling analytics, monitoring of performance, and business growth—making it appropriate for firms seeking to modernize their data capabilities and achieve value in complex data.