Description
- 2+ years of scripting language experience
- Strong SQL and debugging skills
- AWS (S3, Glue, EMR, Lambda, Redshift, Athena)
- Strong Python and Pyspark skills
- Understanding of data modelling, ETL, and batch/streaming pipelines
- Experience with version control and CI/CD (Git, CodePipeline)
- Good communication for stakeholder-facing troubleshooting
- Good to have GenAI Skillset, but not mandatory
- Experience with AWS, networks and operating systems
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The Last Mile Org focuses on technology, products, and programs that enable the efficient, safe, and customer-friendly delivery of packages. The DE team within LMAQ builds and maintains the data ecosystem comprises scalable data infrastructure, data pipelines, datasets and tools for Geospatial, Hub, DTO and Safety Orgs. The BIE, Tech, Product, Program teams of these orgs use this ecosystem to generate reporting, analyses, deep dives that create roadmap for last mile products and processes. The data engineering support role focus on providing on-call support, troubleshooting and investigating tickets, conducting root cause analyses, and improving operational health. They are also responsible for fixing issues, communicating with stakeholders, and proactively monitoring alarms and metrics to ensure the overall health of the services they support.
Key job responsibilities
Core Job responsibilities:
1. Monitor & Optimise Redshift clusters:
Monitor Amazon Redshift clusters, identify long-running queries, and optimize them to maintain cluster performance and ensure healthy operational state
2. Monitor Data Pipelines & ETL Jobs
a. Continuously monitor Glue, Airflow, Lambda, Redshift, Spark, EMR and Kinesis jobs.
b. Identify failures, performance degradation, or bottlenecks in real time.
3. Troubleshoot Data Pipeline Failures
a. Diagnose issues in extraction, transformation, loading, schema mismatches, and data quality.
b. Perform impact analysis and apply immediate fixes.
4. Provide continuous support of existing data engineering products/tools/platforms/solutions that DE built and even extend them for new use cases onboard.
5. Handle On-Call / Incident Response
a. Own the end-to-end on-call rotation, respond to PagerDuty alerts, and restore systems within SLA.
b. Work directly with data engineering teams to resolve critical incidents.
6. Conduct Root Cause Analysis (RCA)
a. Perform RCA for every major incident.
b. Document findings and propose long-term preventive solutions.
7. Manage Data Quality & Validation
a. Validate accuracy, completeness, freshness, lineage, and schema consistency
8.Optimize Queries & Performance
a. Optimize inefficient SQL (Athena/Redshift/Presto/Spark).
b. Tune warehouse performance, resolve WLM queue issues, and reduce compute cost.
9.Maintain Metadata, Catalogs & Schemas
a. Manage Glue Catalog, partition refresh, schema evolution, table permissions, and lineage.
b. Ensure smooth integration between S3, Glue, Athena, Redshift, and Lake Formation.
10.Support Deployments & Release Management
a. Assist in promoting ETL jobs, model code, and pipeline configurations through CI/CD.
b. Validate deployments and perform rollback when necessary.
11. Collaborate with BI, Product & Stakeholders
a. Work with BI teams, analysts, PMs, and upstream/downstream owners.
b. Provide data accessibility support & answer data troubleshooting queries.
12. Maintain Documentation & SOPs
a. Maintain playbooks, runbooks, troubleshooting guides, and data dictionaries.
b. Ensure knowledge transfer and training for new team members.
- Strong SQL and debugging skills
- AWS (S3, Glue, EMR, Lambda, Redshift, Athena)
- Strong Python and Pyspark skills
- Understanding of data modelling, ETL, and batch/streaming pipelines
- Experience with version control and CI/CD (Git, CodePipeline)
- Good communication for stakeholder-facing troubleshooting
- Good to have GenAI Skillset, but not mandatory
- Experience with AWS, networks and operating systems
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The Last Mile Org focuses on technology, products, and programs that enable the efficient, safe, and customer-friendly delivery of packages. The DE team within LMAQ builds and maintains the data ecosystem comprises scalable data infrastructure, data pipelines, datasets and tools for Geospatial, Hub, DTO and Safety Orgs. The BIE, Tech, Product, Program teams of these orgs use this ecosystem to generate reporting, analyses, deep dives that create roadmap for last mile products and processes. The data engineering support role focus on providing on-call support, troubleshooting and investigating tickets, conducting root cause analyses, and improving operational health. They are also responsible for fixing issues, communicating with stakeholders, and proactively monitoring alarms and metrics to ensure the overall health of the services they support.
Key job responsibilities
Core Job responsibilities:
1. Monitor & Optimise Redshift clusters:
Monitor Amazon Redshift clusters, identify long-running queries, and optimize them to maintain cluster performance and ensure healthy operational state
2. Monitor Data Pipelines & ETL Jobs
a. Continuously monitor Glue, Airflow, Lambda, Redshift, Spark, EMR and Kinesis jobs.
b. Identify failures, performance degradation, or bottlenecks in real time.
3. Troubleshoot Data Pipeline Failures
a. Diagnose issues in extraction, transformation, loading, schema mismatches, and data quality.
b. Perform impact analysis and apply immediate fixes.
4. Provide continuous support of existing data engineering products/tools/platforms/solutions that DE built and even extend them for new use cases onboard.
5. Handle On-Call / Incident Response
a. Own the end-to-end on-call rotation, respond to PagerDuty alerts, and restore systems within SLA.
b. Work directly with data engineering teams to resolve critical incidents.
6. Conduct Root Cause Analysis (RCA)
a. Perform RCA for every major incident.
b. Document findings and propose long-term preventive solutions.
7. Manage Data Quality & Validation
a. Validate accuracy, completeness, freshness, lineage, and schema consistency
8.Optimize Queries & Performance
a. Optimize inefficient SQL (Athena/Redshift/Presto/Spark).
b. Tune warehouse performance, resolve WLM queue issues, and reduce compute cost.
9.Maintain Metadata, Catalogs & Schemas
a. Manage Glue Catalog, partition refresh, schema evolution, table permissions, and lineage.
b. Ensure smooth integration between S3, Glue, Athena, Redshift, and Lake Formation.
10.Support Deployments & Release Management
a. Assist in promoting ETL jobs, model code, and pipeline configurations through CI/CD.
b. Validate deployments and perform rollback when necessary.
11. Collaborate with BI, Product & Stakeholders
a. Work with BI teams, analysts, PMs, and upstream/downstream owners.
b. Provide data accessibility support & answer data troubleshooting queries.
12. Maintain Documentation & SOPs
a. Maintain playbooks, runbooks, troubleshooting guides, and data dictionaries.
b. Ensure knowledge transfer and training for new team members.
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