AWS Data Engineer (Senior)

Team: Data Engineering and Data Science

Location: Mumbai, MH

Commitment: Full time (Remote)

Workplace Type: remote

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

We run a forward-deployed model. Senior engineers embed with the customer's team, own outcomes from discovery through production, and carry the delivery commitment personally fixed dates, with Mactores absorbing overage cost for delays inside our control. Aedeon absorbs scale; you absorb judgment. That means less of your week goes to inventory spreadsheets and manual validation, and more goes to architecture, data modeling, and cutover strategy. The role is fully remote. The culture is casual and steers clear of rigid corporate habits. We measure ourselves by what ships.