Data Engineer, AWS Marketing D:SE

- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $91,200/year in our lowest geographic market up to $185,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

Would you like to support increasing customer base and the revenue for AWS, a market-leading cloud offering? Would you like to be part of a team focused on increasing awareness and adoption of the AWS platform by analyzing customer's behavior on and outside AWS websites? Do you want to empower our AWS Marketing organization make data-driven decisions that further establish AWS as leader in the cloud computing world?

As a Data Engineer at AWS, you will be working in a large, extremely complex and dynamic data warehousing environment. We are looking for someone with the uncanny ability to integrate multiple heterogeneous data sources with AWS Marketing Data Warehouse - Jarvis and build efficient, flexible, and scalable data warehouse and reporting solutions. You should be enthusiastic about learning new technologies and be able to implement solutions using these technologies to enable upgrades of the existing platform. You should have excellent business and communication skills and be able to work with business owners to develop and define key business questions, then build the data sets that answer those questions. You should be expert at designing, implementing, and operating stable, scalable, low cost solutions to flow data from production systems into the data warehouse and into end-user facing reporting applications. Above all you should be passionate about working with huge data sets and someone who loves to bring datasets together to answer business questions and drive growth.

At AWS, you have control over every layer you build. Instead of owning a small slice of an existing service, you will own a core segment of a growing marketing platform serving 1000s of internal customers and millions of external customers. You will build on multiple AWS services and have opportunities to engage directly with those teams to improve our core offerings. At AWS, we work with our customers on a daily basis to prove out our ideas, gather feedback, and improve the platform.

Key job responsibilities
- Design, implement, and support a platform providing ad-hoc access to large datasets
- Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL
- Build robust and scalable data integration (ETL) pipelines using SQL, Python and AWS services such as Data Pipelines, Glue
- Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL/Redshift
- Interface with business customers, gathering requirements and delivering complete reporting solutions
- Build and deliver high quality datasets to support business analyst and customer reporting needs
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers

A day in the life
- Working closely with BI engineers, software developers, product managers and other business stakeholders to solve unique business problems.
- Leverage new cloud architecture and data engineering patterns to ingest, transform and store data.
- Build and deliver high quality data solutions to support analysts, engineers and data scientists.

About the team
The core mission of Amazon Web Services (AWS) Marketing is to educate customers (engineers, CTOs, CIOs, and CEOs) about AWS services, and empower them on their journey to the cloud. Our services act as the foundation for announcing new AWS products and are uniquely positioned to redefine how our cloud community consumes information and engages with AWS.

The AWS Marketing, Data Science and Engineering team builds data, experimentation and measurement products, AI/ML models for targeting and segmentation, and self-service insights capabilities for AWS Marketing. We are the central data and science organization, and we work with different teams in AWS Marketing to drive better measurement, improve data access and analytical self-service, deploy and test AI/ML-powered targeting models, and empower strategic decisions with business deep dives.

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