AWS Data Engineer (Senior)

About the role

Production systems replace stalled pilots and diagrams when you drive data infrastructure modernization on AWS. Target architecture and data model decisions are owned by you while Aedeon handles repetitive discovery and validation. Outcomes are measured in production usage once legacy systems are retired through your cutover guidance.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

You compress months of stalled effort into weeks by consolidating and migrating data infrastructure on AWS. Measured outcomes in production replace unused warehouses and pipelines that nobody trusts when your work lands systems. Data models and target architecture are shaped by your decisions where agents cannot advance. Safe cutover on committed dates is enabled by the calls you make during design and implementation. You clear the path for measured outcomes by removing untrusted pipelines and idle warehouses from customer environments.

Requirements

You consolidate and migrate data infrastructure on AWS in weeks, not quarters, at lower engagement cost than traditional consulting. Ownership of target architecture, data model decisions, and pipeline design is required under live constraints. You build with PySpark and SQL on EMR and Glue while modeling for Redshift, Snowflake, Athena, and Presto. Orchestration with Airflow ensures work reaches production systems rather than remaining in reports. You make cutover calls that keep migration schedules aligned with the dates promised in the contract. Customers approach you after data programs stall with pipelines nobody trusts and warehouses that run no new workloads.

Practical notes

The role operates remotely and full-time from Seattle within the Data Platform Modernization pillar. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data platform engineering relies on cloud infrastructure, workflow orchestration, and transformation tools to deliver reliable analytics. Modern data stacks often combine managed services with open source frameworks to handle scale and complexity. Engineering workflows balance automated generation with human judgment for critical decisions. Tool proficiency supports rapid iteration while maintaining production stability. General collaboration aligns engineering, product, and operations around shared outcomes.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Mactores is the agent-native AWS modernization firm. We ship AWS modernization to production in weeks, data platforms migrated, legacy applications and databases refactored, AI agents running against real data, for mid-market and lower-enterprise companies in financial services, healthcare and life sciences, internet and software, manufacturing, and TMEGS.