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Data Engineer II, Sales Planning and Compensation (SPC)

Amazon - Dallas, Texas, USA

Posted May 7, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Salary
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401(k) match
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Market context

Median wage (BLS OEWS)
$111,944 national median
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

39% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
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Where they hire

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About this role

Data Engineer II, Sales Planning and Compensation (SPC) Dallas, Texas, USA Application deadline: Jun 16, 2026 Amazon Web Services is seeking a talented and innovative Data Engineer II to design, build, and evolve critical data capabilities for Sales Planning and Compensation (SPC). SPC delivers secure, highly available, scalable, and high-performance data solutions that empower our field teams to deliver exceptional service to AWS customers. Join a dynamic, high-impact team at an exciting stage of product evolution. You'll help shape the future of how AWS manages and derives value from data at massive scale, while collaborating with product managers, program leaders, data scientists, and cross-AWS technical partners. As a Data Engineer II, you'll own end-to-end data solutions - from ingestion and transformation to analytics and insight generation - while incorporating modern generative AI practices to enhance efficiency, automation, and decision-making. Key job responsibilities - Design and implement robust, scalable data pipelines and ETL/ELT processes using AWS-native services (e.g., Glue, Lambda, EMR, Kinesis, S3, Redshift/Spectrum). - Build and maintain data models, schemas, and storage solutions across relational (SQL) and NoSQL databases, data lakes, and warehouses. - Develop, automate, and optimize metrics, reports, dashboards, and analytics workflows to drive business insights and data-informed decisions. - Own infrastructure for data processing and analytics (e.g., Redshift clusters, Spectrum, EMR), including performance tuning, cost optimization, and architectural evolution. - Leverage **Amazon Bedrock**, **Nova models**, **Amazon Q**, **Kiro**, and other internal AWS GenAI services to prototype intelligent features, automate data workflows, enhance data quality, and accelerate insight

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