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Expect AWS architecture and data pipeline design, reliability/cost questions, and scenarios around migrating or operating large data platforms (e.g., managed AWS services). Be ready to discuss senior-level tradeoffs, stakeholder communication, and production ownership.
Strong fit if you have senior AWS data engineering depth and enjoy client-facing modernization work. Less ideal if you want a pure product-only role or non-cloud stack. Location is United States (not flagged remote in the listing).
As a Senior AWS Data Engineer at Mactores, a typical day centers on designing and operating cloud data pipelines on AWS—ingestion, transforms, and reliable delivery for client platforms. Work may involve managed AWS data services, performance and cost tradeoffs, and collaborating with client or delivery teams on production data systems, consistent with Mactores’ AWS modernization and enterprise automation focus.
Strengthen AWS data services, SQL/ETL, and production ops. Free paths: freeCodeCamp Full Stack (free); Saylor CS courses (free); CS50 (free to learn); Microsoft Learn (free to learn)—pair with hands-on AWS data projects.
The listing is United States with remote set to no; confirm work arrangement with Mactores.
Public coverage stresses AWS-native work, enterprise automation/GenAI, and data platform modernization (e.g., Flipboard on AWS).
None are specified in the listing; free learning resources above can support fundamentals—AWS hands-on experience matters more for this title.
Website: mactores.com
Public cache only — not an employee review.
Mactores is hiring a Senior AWS Data Engineer to build and scale modern data platforms for our customers across the United States. If you thrive on designing reliable, cloud-native data pipelines and turning complex datasets into accessible insights, this full-time role is for you. About the Role As a Senior AWS Data Engineer at Mactores, you will own the lifecycle of data pipelines on Amazon Web Services — from ingestion and transformation through modeling and query optimization. Working hands-on with PySpark, SQL, and a broad AWS analytics stack, you will partner with cross-functional teams to deliver automated, secure, and high-performing data solutions. Key Responsibilities Engineer and operate data pipelines using Amazon EMR or Amazon Glue. Design data models and enable end-user querying through Amazon Redshift or Snowflake, Amazon Athena, and Presto. Orchestrate and schedule pipeline workflows with Apache Airflow. Author and maintain PySpark and SQL scripts to extract, transform, and load data. Work alongside other teams to understand their data requirements and shape effective solutions. Diagnose, tune, and continuously improve the performance of pipelines and data models. Translate technical solutions into clear documentation for both technical and non-technical audiences. Track emerging AWS data technologies and assess how they could strengthen existing systems. Qualifications Bachelor's degree in Computer Science, Engineering, or a comparable discipline. 3+ years of hands-on experience with PySpark and SQL. 2+ years building and maintaining pipelines on Amazon EMR or Amazon Glue. 2+ years of data modeling and querying with Amazon Redshift or Snowflake, Amazon Athena, and Presto. 1+ years orchestrating pipelines with Airflow. Strong analytical, troubleshooting, and problem-solving abilities. Clear communicator who collaborates well and works effectively both independently and on a team. Preferred: AWS Data Analytics Specialty Certification and familiarity with Agile development. About Mactores Since 2008, Mactores has been a trusted partner in modern data platform solutions, helping businesses accelerate value through automated, agile, and secure end-to-end data services. We guide customers through assessment, migration, and modernization on their digital transformation journeys, and our culture is shaped by core leadership principles that champion curiosity, ownership, and collaboration. How to Apply Ready to grow your career…
Generated for personal interview prep · 2026-07-16 UTC · getajob.ai