Data Engineer II, AWS Analytics Engineering

Key job responsibilities

• Design and build reusable, configuration-driven ingestion frameworks and abstractions — not one-off pipelines — that automatically onboard new AWS services and data sources at launch without manual intervention.
• Architect for scale-to-N, ensuring the platform grows with AWS's expanding service portfolio.
• Implement event-driven architectures and self-registration mechanisms that enable service teams to onboard data sources through standardized, self-service interfaces.
• Build and operate big data procurement pipelines at petabyte scale — ensuring reliability, fault-tolerance, cost-effectiveness, and performance across thousands of concurrent ingestion jobs.
• Establish governance, compliance, and audit controls across the ingestion platform — enforcing full data lineage, provenance tracking, and security/data integrity standards for every source flowing through the system.
• Identify and resolve data quality issues proactively.
• Implement automated guardrails, data certification standards, and SLAs for ingestion completeness, freshness, and accuracy.
• Produce high-quality platform code that is secure, maintainable, and understandable by engineers unfamiliar with the system.
• Make sound architectural trade-offs balancing pragmatic short-term delivery with sustainable long-term design.
• Contribute to infrastructure decisions within the team's data architecture.
• Efficiently manage compute, storage, and AWS infrastructure resources — building solutions that are stable, performant, and cost-optimized.
• Own operational health for ingestion systems — monitoring, alarming, runbooks, on-call rotation, and incident resolution.
• Drive automation to reduce operational toil.
• Drive engineering best practices through code reviews, design discussions, and operational reviews.
• Establish standards for dependency management and continuous improvement.
• Collaborate cross-functionally with upstream service teams and downstream analytics consumers.
• Resolve differing technical perspectives and build consensus on platform direction.
• Mentor peers, participate in hiring, and contribute to team knowledge-sharing.

Basic 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 data modeling experience
• 3+ years of SQL experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience implementing data governance controls: access management, encryption, data classification, and audit logging

Preferred Qualifications

• 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 building compliance and audit frameworks: automated data lineage tracking, retention policy enforcement, and regulatory audit support