Spry Squared is a Minority and Woman Owned Small Business headquartered in Denver, Colorado with offices across the United States of America. We are an experienced federal government and commercial service provider with security cleared personnel working on various projects across the USA and the globe. Spry Squared provides organizations with Best in Class Enterprise Solutions, Managed IT Services, Cybersecurity Solutions, IT Professional Services, Recruiting Services, Project/Program Management and technology products. We are your strategic partner and value-added reseller, solving complex business challenges by leveraging technology solutions that reduce costs, optimize productivity and minimize risk. Spry Squared is looking for a strong technical Data Engineer to join a team of highly empowered System Administrators, Developers, and Engineers to support the ETL workflows on classified data source networks (NIPR, SIPR, JWICS).

Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine-learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.

Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put it simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine Learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.

Matillion, the leading provider of data transformation for cloud data warehouses (CDWs) announced that Uberflip has deployed Matillion ETL for Snowflake. By implementing cloud-native ETL, Uberflip reduced data preparation time from five weeks to just one day, helping their product, marketing, and sales teams to rapidly deliver better business value for customers. Matillion ETL delivered repeatable and scalable processes and models for data orchestration, and decreased required development time, freeing up valuable engineering resources. Uberflip is a leading content experience platform and software that enables marketers to create digital experiences with content for every stage of the buyer journey. To better serve internal teams and external customers, Uberflip's data scientists needed a solution that could help them rapidly extract, load, and transform data to scale analysis within the product, empower internal teams for self-service, and get real-time, accurate data from all sources.