Data Migration Engineer

Applied Systems•Chicago, IL

1d•$60,000 - $90,000•Remote

About The Position

Applied Systems is seeking a Data Migration Engineer to join its Data Services team. The role migrates customer data from competitor and legacy systems into Applied Systems SaaS products while supporting business continuity, data quality, and customer satisfaction. The engineer works with new and existing customers to deliver customized migration solutions and collaborates with teams across Onboarding and Success Services, Development, Operations, Solution Services, and Support. The position owns assigned tasks and tickets as part of a global Data Services team that uses a Follow the Sun delivery model. It involves executing components of end-to-end data conversions, producing sample and final deliveries with SQL-based tooling, and supporting data extraction, mergers, transformation, cleanup, and validation. The engineer also develops repeatable scripts and workflows, maintains documentation, analyzes customer data and competitor system structures, and contributes to custom solutions and continuous improvement. As tools and processes evolve, the role may increasingly use cloud data platforms such as BigQuery and emerging automation and intelligent tooling. Success requires attention to detail, adaptability, effective cross-functional communication, and the ability to work independently while coordinating handoffs across regions and time zones.

Requirements

Strong foundational knowledge of database structures and SQL, with demonstrated real-world experience in data migration, integration, or transformation scenarios.
Solid understanding of ETL concepts, data mapping, and data quality best practices.
Ability to work collaboratively in a global team environment where work is shared across regions to maintain continuous delivery.
Ability to manage multiple competing project timelines while remaining detail oriented and customer focused.
Strong ownership and initiative, balanced with the ability to align work with shared team standards, processes, and delivery goals.
Clear communication skills and ability to collaborate with engineering teams and non-technical stakeholders.
Bachelor’s degree or equivalent practical experience in Information Systems, Computer Science, Data Science, Software Engineering, or a related discipline.

Nice To Haves

Hands-on experience supporting SaaS data migration or ETL initiatives in a production environment.
Experience or exposure to Google Cloud Platform, including BigQuery and Cloud Storage.
Experience with automation frameworks, scripting, or intelligent tools for data analysis, migration, or quality assurance.
Interest in evaluating, validating, or refining AI-assisted or agent-based workflows for future data migration capabilities.
Experience using Jira, SharePoint, or similar tools to track work, collaborate across teams, and document technical processes.

Responsibilities

Own and execute assigned components of end-to-end data conversion and migration efforts from legacy systems to Applied Systems SaaS products, producing sample and final deliveries using SQL-based tooling.
Perform data extractions, acquisition mergers, transformations, and data cleanup and quality services for varied customer scenarios and business requirements.
Develop standardized, efficient, repeatable data scripts and workflows; audit and validate data accuracy, integrity, and consistency throughout the migration lifecycle.
Work with customers and internal stakeholders to understand requirements, manage expectations, and deliver outcomes that maintain business continuity.
Complete work independently while supporting smooth handoffs and continuity across teams operating in different regions and time zones.
Adapt execution approaches to changing project plans, timelines, rescheduling, and contingency plans.
Collaborate with Development teams on testing, defect resolution, enhancements, and implementation of evolving solutions; maintain clear and accurate Data Services documentation.
Analyze customer data and competitor system structures to identify opportunities to improve transformation efficiency and solution quality.
Use evolving cloud-based data platforms such as BigQuery to support migration workflows and downstream integrations.
Use emerging automation and intelligent tooling to improve migration speed, scalability, and quality, and provide feedback on responsible adoption.
Contribute to custom solution delivery and continuous improvement initiatives for Data Services capabilities, tooling, and processes.