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Mactores is seeking a Data Engineer Intern to support their data platform modernization efforts, focusing on transitioning legacy systems to AWS. In this role, you will engage in hands-on work by writing pipeline code for actual projects, collaborating with business leads, analysts, and data scientists to grasp the domain and develop data products that enhance decision-making. The internship emphasizes learning about data quality and the importance of metrics in business contexts, with a focus on tools like Spark and ETL design. While you will not be responsible for cutover tasks, you will gain valuable experience in the agent-native approach, which streamlines repetitive tasks traditionally assigned to junior engineers. This position is fully remote, allowing for flexibility in your work environment. The role is ideal for individuals passionate about data quality and eager to contribute to scalable solutions. No specific qualifications or prior experience are mentioned, making it suitable for those looking to start their career in data engineering.
Mactores is the agent-native AWS modernization firm. Most modernization work doesn't ship, it stalls in pilots, slips a year, or lands at three times the budget. We exist to ship it: production systems running, legacy retired, outcomes measured. Our delivery is built on Aedeon, the agent platform built by Mactores' founders' sister company, which absorbs the repetitive 60–70% of engagement work, discovery, dependency mapping, validation, test generation, that traditional consulting bills human hours against. Forward-deployed engineers own the rest: architecture, judgment, and cutover, on dates we commit to in the contract. This internship is an apprenticeship in data platform modernization the pillar of our work where legacy warehouses get retired and data platforms reach production on AWS. You'll write real pipeline code on real projects, working with business leads, analysts, and data scientists to understand the domain, then with engineers to build data products that make decisions better. Here's the honest framing: you're learning the agent-native craft, not owning cutover. Agents handle much of the repetitive discovery and validation work that used to fill junior engineers' days, which means your time goes further into Spark, ETL design, and understanding why data quality decisions matter to the business. If you care about the quality of the metrics a business runs on, and you want your solutions to scale to bigger questions, this is the seat.
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