Strategic Data Architect for Cross‑Domain Platforms

Amazon Web Services (AWS)

Seattle (WA)

On-site

USD 155,000 - 209,000

Full time

14 days+

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Job summary

Amazon Web Services, Inc. is seeking a Senior Data Engineer to join our Strategic Initiatives team.

You will own data architecture for internal deal intelligence, unifying AWS revenue, vendor spend, contracts into a single decision framework that informs portfolio decisions. Work involves ingesting data from thousands of buy‑side agreements and dozens of upstream systems, designing data models, building scalable pipelines, and enabling analytics, forecasting, and ML layers while collaborating

Qualifications

5+ years of data engineering experience.
Experience with data modeling, warehousing and building ETL pipelines.
Experience with distributed data processing using Spark (or equivalent) and workflow orchestration (e.g., Airflow).
Strong SQL skills and proficiency in at least one scripting language (Python preferred) for data manipulation and pipeline development.
Experience designing data schemas and operating data stores in support of analytics and downstream consumers.
Experience working with cloud data services (AWS preferred).

Responsibilities

Own the data architecture for your domain areas (e.g., ingestion, entity resolution, vendor relationship modeling) and contribute to broader platform architecture decisions.
Deliver with limited guidance where logical data models and end‑to‑end data flows are not yet defined.
Onboard and integrate disparate data sources from across Amazon (AWS, Retail, Procurement, Finance, vendor systems); resolve conflicts across inconsistent real‑world data and drive cross‑team alignment on data definitions and ownership.
Evolve data sourcing and modeling toward governed, reusable, directly‑consumed data products; drive the migration within your domains without disrupting downstream consumers.
Build and operate pipelines across distributed processing, orchestration, and an open‑lakehouse foundation on AWS (e.g., Glue/Spark, Airflow, Iceberg, Athena), governed with Lake Formation and cross‑account IAM.
Raise operational excellence: incremental and retry‑safe loads, atomic publication, dependency‑aware scheduling, and data‑quality validation.
Design data systems within your scope that serve diverse access patterns (batch analytics, governed BI, ML training datasets).
Partner with the science team to build the data infrastructure for forecasting and reinforcement‑learning work, including feature pipelines, training datasets, decision logs, and reward signals.
Drive engineering and operational excellence best practices across the data infrastructure
Mentor and develop peers
Make trade‑offs between short‑term delivery needs and long‑term architectural scalability