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.
Life at Mactores
We care about creating a culture that makes a real difference in the lives of every Mactorian. Our 10 Core Leadership Principles that honor Decision-making, Leadership, Collaboration, and Curiosity drive how we work.
1. Be one step ahead
2. Deliver the best
3. Be bold
4. Pay attention to the detail
5. Enjoy the challenge
6. Be curious and take action
7. Take leadership
8. Own it
9. Deliver value
10. Be collaborative
We would like you to read more details about the work culture on https://mactores.com/careers
The Path to Joining the Mactores Team
At Mactores, our recruitment process is structured around three distinct stages:
Pre-Employment Assessment:
A series of evaluations of your technical proficiency and suitability for the role.
Managerial Interview: The hiring manager engages with you in multiple discussions, 30 minutes to an hour each, covering technical skills, hands-on experience, leadership potential, and communication.
HR Discussion: During this 30-minute session, you'll have the opportunity to discuss the offer and next steps with a member of the HR team.
Mactores provides equal opportunities in all employment practices. We don't discriminate based on race, religion, gender, national origin, age, disability, marital status, military status, genetic information, or any other category protected by federal, state, and local laws. This applies to every part of the employment relationship, recruitment, compensation, promotions, transfers, disciplinary action, layoff, training, and social and recreational programs.
Note: Please answer as many questions as possible with this application to accelerate the hiring process.
- Write efficient code in the technology chosen for the project — Spark or Apache Beam, for example.
- Explore new technologies and learn new techniques to solve business problems creatively.
- Collaborate across engineering and business teams to build better data products and services.
- Deliver projects with the team and keep customers updated on time — shipping on schedule is a habit you'll build early here.
- Exposure to Apache Spark.
- Exposure to ETL concepts using pySpark and SparkSQL.
- Exposure to SQL queries and stored procedures, and the appetite to work on challenging projects with a mentor-oriented leader.
- Prior experience in working on AWS EMR, Apache Airflow
- AWS Certified Big Data – Specialty certification, Azure Certification, Snowflake Certification
- Cloudera or Hortonworks Certified Big Data Engineer
- Understanding of DataOps Engineering
Mactores delivers through agents plus forward-deployed engineers. Aedeon, the agent platform we deploy, does the repetitive majority source discovery, schema mapping, validation harnesses while engineers make the architecture and cutover calls. As an intern you work inside that model from day one: you'll see how a data platform actually reaches production, and you'll learn to work with agents as tooling rather than treating them as a threat or a magic trick. The engineers who grow fastest here are the ones who master that combination.