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# Senior Manager, Senior Data Engineer - Paid...

- **Company:** Marriott International, Inc.
- **Location:** Bethesda, MD, United States (Remote available)
- **Experience:** Expert
- **Salary:** $100,400.0 - $181,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Cloud Database, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Systems, Distributed Data Store, Marketing Information Systems, Cloud Services, Cloud Platform System, Snowflake, Data Pipelines
- **Published:** June 20, 2026
- **Apply:** https://www.juju.com/job/00000000g9ywo6

## About the Role

+ Strong experience building cloud-based data solutions (AWS + Snowflake preferred).

 + Hands-on expertise in data pipeline orchestration (Airflow) and modern ETL/ELT patterns.

 + Advanced proficiency in SQL and data modeling for analytics and reporting use cases.

 + Solid understanding of data governance, quality, and security principles.

 + Experience working with large-scale, distributed data systems supporting analytics or personalization.

 + Strong collaboration skills with ability to work across engineering, product, and business teams.

## Description

Responsible for designing and delivering scalable data pipelines and data solutions for the Marketing & Digital (Paid / Owned) domain, enabling personalization, campaign optimization, and enterprise analytics. This role contributes to the development of high-quality, governed data products on Marriott's cloud-native data platform (Snowflake, Airflow, AWS)., + Design and implement end-to-end data pipelines (ingestion, transformation, and delivery) using Airflow and Snowflake to support marketing data use cases.

 + Develop and optimize data models and curated datasets to enable analytics, reporting, and downstream data products.

 + Collaborate with engineering, product, and business teams to translate requirements into scalable data solutions aligned with platform standards.

 + Ensure data quality, governance, security, and compliance across pipelines and datasets.

 + Improve performance, reliability, and cost efficiency of Snowflake workloads and orchestration workflows.

 + Contribute to platform modernization efforts, including automation, standardization, and reusable pipeline frameworks.

 + Support and mentor engineers by promoting best practices in data engineering, testing, and deployment.


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