AWS Data Engineer (Senior)

About the role

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 is a senior role in our Data Platform Modernization pillar: consolidating and migrating customer data infrastructure on AWS in weeks, not quarters, at meaningfully lower engagement cost than traditional data consulting. Customers come to us after a data program has stalled pipelines nobody trusts, warehouses nobody runs new workloads on, a modernization that produced diagrams instead of production systems.

Aedeon handles automated source discovery, schema mapping, lineage extraction, and parallel-run validation. You own what agents can't: target architecture, data model decisions, pipeline design under real constraints, and the calls that make a cutover safe. You'll build with PySpark and SQL on EMR and Glue, model for Redshift, Snowflake, Athena, and Presto, orchestrate with Airflow and your work will reach production, not a slide deck.

What you will do? - Build and maintain data pipelines on Amazon EMR or Amazon Glue that run in production.

- Design data models and end-user querying on Amazon Redshift or Snowflake, Amazon Athena, and Presto.

- Build and maintain pipeline orchestration with Airflow.

- Work with customer and internal teams to understand data needs and design the solutions that meet them.

- Troubleshoot and optimize pipelines and data models until they hold up under real load.

- Write and maintain PySpark and SQL scripts to extract, transform, and load data.

- Document and communicate technical decisions to technical and non-technical audiences — customers sign off on what we ship.

- Track new AWS data technologies and judge their impact on the systems we run.

What are we looking for? - Bachelor's degree in Computer Science, Engineering, or a related field.

- 3+ years of experience working with PySpark and SQL.

- 2+ years of experience building and maintaining data pipelines using Amazon EMR or Amazon Glue.

- 2+ years of experience with data modeling and end-user querying using Amazon Redshift or Snowflake, Amazon Athena, and Presto.

- 1+ years of experience building and maintaining pipeline orchestration using Airflow.

- Strong problem-solving and troubleshooting skills.

- Excellent communication and collaboration skills.

- Ability to work independently and within a team environment.