Data Engineer II, AWS Analytics Engineering - FDT

Amazon Web Services (AWS) operates a large-scale cloud data platform that supports business decisions across AWS services. The Data Engineer II will independently own significant data architecture responsibilities, building logical data models, ingestion flows, analytical pipelines, and data quality solutions while applying secure, scalable, and maintainable engineering practices.

Responsibilities

Identify and resolve data quality issues in processing tools, contribute to improvements and innovation, and ensure best practices in pipelines you design and maintain. For example: optimizing ingestion flows for new data sources, or building efficient transformation patterns within the team's domain

Build and optimize logical data models and data pipelines for difficult datasets — ensuring solutions are testable, maintainable, and efficient while addressing security, scalability, and cost considerations

Make appropriate technical trade-offs at the dataset level, balancing pragmatic short-term decisions with sustainable long-term approaches

Produce high-quality code — solutions that are pragmatic, secure, maintainable, and flexible without over-engineering. Write code that engineers unfamiliar with the system can understand. Limit the use of short-term workarounds and minimize incidental complexity

Contribute to infrastructure decisions within the team's data architecture. Efficiently manage resources (system hardware, data storage, query optimization, AWS infrastructure) and build solutions that are stable and performant

Solve difficult problems — for example, designing data models that integrate multiple sources within the team's domain, or combining datasets to unlock new analytical capabilities. Identify issues that may lead to data inconsistency or gaps in data quality, and proactively resolve them

Break down project work into manageable tasks, deliver independently, and collaborate effectively with peers on shared dependencies. Resolve discordant views and build consensus among team members

Mentor peers, participate in hiring, and contribute to team knowledge-sharing

Drive data engineering best practices within the team — code quality, data certification, dependency management, and operational excellence. Establish SLAs, automate manual processes, and improve self-service access to data

Drive improvements through code reviews, design discussions, team planning, and operational reviews

Participate in on-call rotation and take ownership of operational health for data systems you own — contribute to monitoring, alarming, runbooks, and incident resolution

Preferred

Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Experience with big data technologies such as: Hadoop, Hive, Spark, EMR

Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage

4+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience

Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience with software development lifecycle

Amazon S3

AWS Glue

Amazon EMR

Amazon Kinesis

Amazon Data Firehose

AWS Lambda

AWS IAM

IAM Roles

Permissions

Non-Relational Databases

Object Storage

Document Stores

Key-Value Stores

Graph Databases

Column-Family Databases

Hadoop

Hive

Spark

Artificial Intelligence

Machine Learning

Generative AI

Database Technologies

Storage Technologies

Data Warehouse Architecture

Infrastructure Components

ETL

ELT

Reporting Tools

Analytical Tools

Data Environments

Data Structures

Distributed Systems

Data Extraction

Data Ingestion

Data Processing

Large-Scale Data Processing

Software Development Lifecycle

Benefits

Sign-on payments

Restricted stock units (RSUs)

Health insurance, including medical, dental, vision, and prescription coverage

Basic Life & AD&D insurance

Option for Supplemental life plans

Employee Assistance Program (EAP)

Mental Health Support

Medical Advice Line

Flexible Spending Accounts

Adoption and Surrogacy Reimbursement coverage

401(k) matching

Paid time off

Parental leave

Company

Online retail and cloud computing services

Seattle, Washington, USA

https://amazon.com/

Founded in 1994

10001+ employees

Qualifications

5+ years of data engineering experience

3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience

3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience

5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience

3+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies and Data Modeling Experience

Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy

Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Experience with big data technologies such as: Hadoop, Hive, Spark, EMR

Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage

4+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience

Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience with software development lifecycle

Required skills

AI/ML, AWS Glue, AWS IAM, AWS Lambda, Amazon EMR, Amazon Kinesis, Amazon Redshift, Amazon S3, Apache Spark, Data Engineering, Data Modeling, Data Warehousing, Distributed Data Processing, ETL/ELT, Hadoop, Hive, NoSQL Databases, OLAP, SQL

Experience

5+ years