Data Engineer, GSSO Capacity Planning, GS Support Capacity Planning

Data Engineer, GSSO Capacity Planning, GS Support Capacity Planning

Job ID: 2756203 | Amazon Web Services, Inc.

Amazon Web Services (AWS) provides a scalable cloud computing platform to companies globally. AWS Global Services (GS), formed in 2022, delivers customer success throughout the cloud adoption lifecycle. Our 25K+ employees and integrated offerings enable us to combine technology and human expertise to maximize and accelerate customer outcomes. GS is comprised of four primary business units: 1) Global Services Security (GSS) provides security guidance and offerings, 2) Training & Certification (T&C) offers cloud skills training and certification, 3) Professional Services (ProServe) provides consulting and hands-on-keyboard services, and 4) Support and AWS Managed Services (Support) delivers 24/7 technical support and managed services. Together, these teams continuously improve our systems and processes to enable better results for both customers and employees, with the GS Strategy & Operations (GSSO) teams supporting each. GSSO enables integrated business support, product management, planning, and deal strategy for GS. GSSO understands customer experiences and inspires bold ideas to deliver the best experiences and solutions to our customers. We embrace scientific thinking, pursue continuous improvement, and develop talent to provide world-class support across GS.

Position Overview: We are seeking an experienced and skilled Data Engineer to join our dynamic team. The Data Engineer will play a crucial role in designing, developing, and maintaining efficient and scalable data pipelines, data models, and data warehousing solutions. This position will be responsible for ensuring data integrity, quality, and availability across the organization, enabling data-driven decision-making and supporting business intelligence initiatives.

Key job responsibilities

1. Conduct data discovery and profiling for various data sources, including Page0, entitlement models, and propensity models.
2. Design and implement robust ETL pipelines to extract data from multiple sources, transform it according to defined business rules, and load it into centralized data storage systems.
3. Develop and maintain efficient data ingestion processes, ensuring timely and accurate data availability.
4. Design and develop effective data models and schemas for Page0 data, entitlement model data, propensity model data, and other relevant data sources.
5. Implement and maintain a centralized data warehouse architecture, ensuring scalability, performance, and data integrity.
6. Collaborate with stakeholders to understand data requirements and translate them into optimized data structures and models.
7. Establish and enforce data governance policies and procedures to maintain data quality, consistency, and integrity across the organization.
8. Implement data quality controls, monitoring, and reporting mechanisms to ensure data accuracy and reliability.
9. Continuously monitor data quality, identify issues, and implement remediation measures as needed.
10. Design and implement centralized data integration pipelines to consolidate data from various sources into a unified data platform.
11. Ensure seamless data integration and interoperability between different systems, applications, and processes.
12. Integrate propensity models with downstream systems and processes, facilitating data-driven decision-making.
13. Implement robust data security measures, including access controls, authentication, and authorization mechanisms, to protect sensitive data.
14. Ensure data lineage and traceability across the organization, enabling auditing and compliance.
15. Continuously monitor data flows and optimize performance by identifying bottlenecks and implementing efficient solutions.
16. Develop and implement data retention and archiving policies to manage data growth and storage requirements.
17. Establish data disaster recovery and business continuity plans to ensure data availability and resilience.

If you are a passionate data professional with a keen interest in building scalable and robust data solutions, we encourage you to apply for this exciting opportunity. Join our team and contribute to driving data-driven innovation and business success.

BASIC QUALIFICATIONS

1. 2+ years of data engineering experience
2. Experience with data modeling, warehousing and building ETL pipelines
3. Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)

PREFERRED QUALIFICATIONS

1. Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
2. Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
3. Experience with at least one modern language such as Java, Python, C++, or C# including object-oriented design

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.