Data Engineer II, AWS Analytics Engineering - FDT

Job description

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

• AWS services: S3, Glue, EMR, Kinesis, Data Firehose, Lambda, IAM

• 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

• Ethic Leaf LLC

• Seattle, Washington, USA

• 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

• Interested in this role?

• Apply directly on the employer's website — it only takes a few minutes.