Data Conversion Engineer

All About Aeries

At Aeries, our mission is to deliver data-driven, intuitive, and innovative solutions that unite learning communities to advance student success. We believe strongly in the supporting role of technology in education but recognize that there’s no replacement for the wisdom and determination of the real people supporting students’ academic successes every day. That’s why our goal is to build tools that allow educators to focus on what they do best: educating.

About the Job

The Data Conversion Engineer is a technical individual contributor role on Aeries’ Implementation and Professional Services team. This person is responsible for designing and building Aeries’ next-generation data conversion platform from the ground up — an AI-assisted tool capable of handling the majority of the conversion lift across any source data format or system type. They will also own the full conversion delivery lifecycle for customer engagements, working directly with district staff and administrators to identify and resolve data complexities that require human judgment and expertise. This role is critical to Aeries’ ability to scale high-quality, efficient district onboarding.

What You’ll Do

• Design and build Aeries’ data conversion platform from scratch, creating an AI assisted ETL tool that handles schema mapping, data transformation, and validation across any source data system or format.
• Maintain and evolve the conversion platform as source schemas change, ensuring it remains capable of handling the full range of data formats and system types Aeries encounters across its customer base.
• Own end-to-end conversion delivery for customer engagements, managing the technical execution from initial data extract through final validation and sign-off.
• Engage directly with district IT staff and data administrators to review conversion outputs, capture data anomalies that fall outside automated handling, and resolve edge cases requiring human judgment.
• Perform source data analysis and schema mapping prior to each conversion, identifying structural inconsistencies, custom fields, and district-specific configurations that require special handling.
• Develop and maintain data validation routines that verify conversion accuracy and data integrity before and after migration to Aeries.
• Leverage AI tools, including Claude Code, to accelerate tool development, automate schema inference, and continuously improve the platform’s coverage and accuracy.
• Document conversion processes, tool architecture, and data mapping approaches to support institutional knowledge and future team growth.
• Collaborate with Integration Engineers, Product, and Engineering teams to surface conversion-related patterns, defects, and enhancement opportunities.
• Contribute to ongoing improvement of the conversion process, identifying opportunities to increase automation rates and reduce manual remediation time.

What You Bring

• Proficiency in Python and SQL, including experience building ETL pipelines, data transformation scripts, and working with relational databases.
• Demonstrated ability to analyze and map complex, unfamiliar data schemas, including experience adapting to varied or undocumented source structures.
• Applied experience with AI-assisted development tools; experience with Claude Code or similar agentic coding tools is strongly preferred.
• Experience building or maintaining software tools, not just using them, including ownership of tool architecture, version control, and iterative improvement.
• Strong data validation and quality assurance practices, with the ability to design and run checks that confirm conversion accuracy at scale.
• Customer-facing communication skills; comfortable leading technical review sessions with district IT staff and translating complex data issues into clear, actionable language.
• Familiarity with K-12 student information systems or other enterprise data platforms commonly used in education is a plus but not required.
• Experience with cloud environments (AWS, Azure, or GCP) and an understanding of how data tools operate in hosted infrastructure.
• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent demonstrated experience. Strong candidates with relevant graduate-level coursework, internship experience, or a portfolio of AI-assisted tooling projects are encouraged to apply.

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