Sr Data Engineer, Vulcan

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- Experience with Redshift and SQL
- Previous experience working with AWS SageMaker

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.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $139,100/year in our lowest geographic market up to $240,500/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.
Are you passionate about using Data as a way to enable automation, knowledge extraction, and artificial intelligence through the use of Machine Learning, Natural Language Processing, Recommender systems, Computer Vision, and Optimization? We are building a team of experienced Engineers to support this vision.

On this team you will partner with a mature team of Software Engineers and Scientists to create the infrastructure and processes needed to support multiple use cases including extracting text, image, and audio to support ML models, analyze and model customer reading behavior to measure engagement and detect risks, study and optimize manufacturing and fulfillment processes, and build AI-based systems.

Our team has green-field opportunities to create the right data infrastructure to support these use cases. We offer autonomy, value end-to-end, ownership, and a strong partnership with our experienced sister teams.

As a Sr Data Engineer, you will help define, design, implement and test the data process and infrastructure to productionalize and monitor ML models for multiple use cases.

As a Sr Data Engineer, you will be working with a large and complex dataset. You should have deep expertise in the design, creation, management, and business use of extremely large datasets. You should be expert at designing, implementing, and maintaining stable, scalable, low cost data infrastructure.

The successful candidate will be an expert with SQL, ETL (and general data wrangling) and have exemplary communication skills. The candidate will need to be a self-starter, comfortable with ambiguity in a fast-paced and ever-changing environment, and able to think big while paying careful attention to detail.

Come join us as we continue to revolutionize the book industry!

Key job responsibilities
Deliver large data solutions in difficult or ambiguous data areas (new or existing systems).
Create data solutions that are easily usable by customers – inventive, secure, maintainable, scalable, and extensible.
Build data solutions that are easy for others to contribute to. Know how to make data auditable, available, and accessible.
Provide significant knowledge/experience with data design approaches and industry technologies.
Evaluate end-to-end data designs for strengths and weaknesses (data quality, scalability, latency, security, performance, data integrity, etc.).
Anticipate data access patterns and remove bottlenecks. Ask the right questions and drive the right technical solution(s).
Split development work into parallel tasks that can be performed by them and others and reassembled successfully.
Influence team technical strategy. Understand that not all problems are new (or require new data solutions). Make appropriate architectural trade-offs. Show good judgment when making technical trade-offs between short-term technology needs and long-term business needs
Take ownership of team’s data architecture and make it simpler. Proactively fix an architecture deficiency. Resolve root cause.
Drive data engineering best practices and set standards.
Understand system limitations, scaling factors, boundary conditions, and/or the reasons for architectural decisions.