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AWS Data Engineer (Senior)
Mactores
AWS Data Engineer (Senior)
This job is still taking applications, but it's been up a while.
About the job
Mactores provides modern data platform solutions for business digital transformation. The senior AWS Data Engineer will design, build, and optimize data pipelines, models, and orchestration workflows.
Requirements
- 3+ years PySpark and SQL experience
- 2+ years with Amazon EMR or Glue
- 2+ years data modeling/querying
- 1+ years Airflow orchestration
- Strong troubleshooting skills
Qualifications
- Bachelor's in CS/Engineering
- Excellent communication skills
- Ability to work independently
- Problem-solving abilities
Full job description
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.
You are preferred if you have
- AWS Data Analytics Specialty Certification
- Experience with Agile development methodology
How we work?
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