Senior Data Engineer, Amazon

• You should be a builder who thinks in terms of architecture, not just code
• You thrive in ambiguity, proactively identify and solve systemic data problems, and set engineering standards that raise the bar for your team
• You bring deep expertise in data modeling, ETL/ELT pipeline design, and distributed data systems, and you combine that with strong business judgment to make the right trade-offs between short-term delivery and long-term platform health
• 5+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with SQL
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
• Experience mentoring team members on best practices

Desirable

• As a Senior Data Engineer on the AppStar DNA team (Data & Analytics Engineering), you will be a technical leader driving our data platform evolution—a strategic effort to modernize our security data infrastructure into a scalable, semantically rich, and operationally excellent data ecosystem
• You will define and own data architecture at the team level, working across multiple data domains to build the foundational data infrastructure that powers the AppStar organization
• This is not a role where you inherit a finished system—you will architect and lead the transformation
• You'll work at the intersection of large-scale data engineering, modern lakehouse architectures, and security domain expertise to build a unified Security Data Model that serves as the backbone for analytics and reporting use cases
• * Define and own the data architecture for security data domains, ensuring data is auditable, available, and accessible
• * Design and implement large-scale ETL/ELT pipelines using SQL, Python, and AWS services (Redshift, Glue, S3, Lambda, Step Functions, Athena, Apache Airflow)
• * Lead the migration and modernization of legacy security data pipelines to modern lakehouse patterns (Apache Iceberg, Spectrum, Lake Formation)
• * Build and maintain the context layer—conformed dimensions, standardized data models, entity models, and data contracts—that serve as the single source of truth for AppStar security data
• * Architect data solutions that enable analytics and insights—ensuring data quality, lineage, and freshness support advanced use cases
• * Drive data engineering best practices across the team: data modeling standards, naming conventions, data quality frameworks, CI/CD for data pipelines, and operational excellence
• * Proactively identify and resolve architecture deficiencies—simplify complex data flows, remove bottlenecks, and reduce technical debt
• * Split development work into parallel tasks, mentor junior engineers, and ensure deliverables come together into a coherent whole
• * Learn and understand a broad range of Amazon's security data resources and know when, how, and which to use
• * Participate in design reviews, on-call rotations, and incident response for production data pipelines