Data Engineer, AWS Marketing D:SE

Key Skills

Aartificial intelligence, Amazon RedShift, Data Engineering, Data pipelines, Data science techniques, ETL/ELT technologies, Large data sets, Machine learning techniques, Python Programming, SQL

Job Description

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.

ABOUT AWS:

Diverse Experiences

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Inclusive Team Culture

Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship and Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

BASIC QUALIFICATIONS

• 3+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines

PREFERRED QUALIFICATIONS

• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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 $118,900/year in our lowest geographic market up to $205,600/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.

Job details

USA, WA, Seattle

AWS Marketing

Data Science

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