Data Engineer II

About AWS Managed Operations (MO)

Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world! The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions; improving the availability, reliability, latency, performance and efficiency to operate AWS regions.

Data Engineer Role Overview

As a Data Engineer, your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft AWS' strategic initiatives. In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across AWS to optimize growth, strategy, and user experience across AWS. This role will sit in our new headquarters in Northern Virginia.

Key Job Responsibilities

• Collaboration and Product Development: Interact with business and software teams to understand their business requirements and operational processes, to inform system design.
• Data Modeling and Architecture: Develop robust data models and architectures that support the organization's data-driven initiatives, ensuring data quality, consistency, and accessibility.
• Data Pipeline Development: Design, build, and maintain efficient, scalable, and reliable data pipelines to ingest, transform, and load data from various sources into a unified data platform.
• Scalability and Performance: Design and implement scalable data solutions that can handle increasing data volumes and support high-performance data access and querying. Design, provision, and maintain scalable data infrastructure on AWS (Glue, Lake Formation, S3) to support ingestion, transformation, and governance of operational data across dev, gamma, and production environments.
• Documentation & Continuous Improvement: Create, enhance, and maintain technical documentation.

A Day in the Life

• Data Engineering: Guide teams in building optimal data artifacts (datasets, visualizations) to address key questions. Refine systems, design solutions, and create scalable data models. Ensure data security and quality while suggesting efficient architecture, development approaches, and data management standards for complex analytical problems.
• Product Leadership: Use data to identify opportunities, and tackle challenges. Ensure data artifacts/components deliver value by supporting AWS strategy and driving innovative solutions.
• Communication and Influence: Tell data-driven stories that convince and influence partners. Build credibility with clear insights, structured recommendations, and serve as a trusted partner within the organization.

About The Team

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

About AWS

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Basic Qualifications

• 3+ years of data engineering experience
• Experience with data modeling, warehousing, and building ETL pipelines
• Knowledge of distributed systems as it pertains to data storage and computing
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

Preferred Qualifications

• 5+ years of data engineering experience
• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

Key skills/competency

• Data Engineering
• Data Modeling
• ETL Pipelines
• Distributed Systems
• Python
• AWS Glue
• S3
• Data Warehousing
• Scalability
• Data Pipelines