亚马逊Data Engineer, AOP - RoW Central Data Engineer Team

任职要求

基本任职资格
- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

优先任职资格
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, e…

工作职责

Design and build data pipelines — architect and implement robust batch, intraday, and near-real-time ETL/ELT pipelines ingesting data from diverse sources including EDX datasets, SNS events, Andes tables, REST APIs, and S3, landing data reliably into Redshift and Aurora.
Own Redshift infrastructure — write, optimize, and tune SQL and ETL workloads on Amazon Redshift; manage WLM queues, distribution/sort keys, materialized views, and query performance; proactively identify and resolve performance bottlenecks.
Manage big data lifecycle — design data models and schemas (star/snowflake), enforce data partitioning and retention policies, implement data quality checks, and ensure data accuracy and freshness SLAs are consistently met.
Deliver on ambiguous requirements — independently break down loosely defined business asks into concrete technical deliverables with clear scope, milestones, and acceptance criteria; drive from requirement to production with minimal hand-holding.
Build automation and self-healing systems — reduce manual toil through automation (Lambda, ECS, Step Functions, CloudWatch alarms); contribute to the team's Server Auto Maintenance Program and ETL cleanup initiatives.
AWS cloud engineering — use AWS services (Redshift, Aurora RDS, S3, Lambda, ECS Fargate, SQS, SNS, CDK/CloudFormation, Secrets Manager, EventBridge) to build scalable, cost-efficient, and maintainable data infrastructure.
Drive cost optimization — proactively identify inefficiencies in SQL workloads, cluster utilization, and pipeline design; propose and implement optimizations that reduce AWS spend without compromising reliability.
Support stakeholders and data consumers — partner with Business Analysts, BI Engineers, Data Scientists, and PMs to understand data needs; deliver clean, documented, raw data pipelines; maintain clear boundaries around pipeline ownership and scope.
Maintain operational excellence — participate in on-call rotation, respond to production incidents with urgency and structured root cause analysis, and implement permanent fixes rather than workarounds. 

A day in the life
A typical day for a DE looks like:
Oncall: Review pipeline/infra/services run status on dashboards; triage any failed jobs or data freshness alerts; provide ETA and updates to stakeholders as needed.
Core hours: Work on active sprint deliverables — this may include writing CDK infrastructure code, developing Redshift SQL models, building a new EDX ingestion pipeline, or debugging a WLM contention issue on the central cluster.
Collaboration: Join a sync with stakeholders to understand a new data onboarding request; push back clearly when scope creep or non-standard pipeline patterns are introduced; document the agreed design in the team wiki.
Deep work: Independent heads-down time on complex tasks — performance tuning a slow Redshift query, infra upgrade, refactoring a Lambda trigger handler, or writing a CDK stack for a new ECS data job etc.
Wrap-up: Update task statuses in SIM tickets; code review a peer's PR; document any patterns or learnings into the team's internal knowledge base.
There is no one telling you exactly what to do each hour — you are expected to manage your own task queue, surface blockers early, and keep work moving forward with accountability.