Data Engineer, Analytics and Insights

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- 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.
We are investing heavily in building an excellent advertising business, and are responsible for defining, and delivering a collection of self-service performance advertising products – “always-on analytics” that is fully scalable and reliable. Our products are strategically important to our leadership, finance, economists, analysts, and BI partners to drive long-term growth. We mine billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative, and fun loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
The Advertising Analytics and Data Management team is looking for an exceptional Data Engineer who is passionate about data and the insights that large amounts of data can provide, who thinks/acts globally, and who has the ability to contribute to major novel innovations in the industry. The role will focus on working with a team of data engineers, business and tech savvy professionals to lay down scalable data architecture to ingest large amounts of structured and unstructured datasets and work with stakeholders to drive business decisions based on these datasets.

The ideal candidate will possess both a data engineering background and a strong business acumen that enables him/her to think strategically and add value to the customer experience. He/She will experience a wide range of problem solving situations, requiring extensive use of data collection and analysis techniques such as data mining and machine learning.

The successful candidate will work with multiple global site leaders, Business Analysts, Software Developers, Database Engineers, Product Management in addition to stakeholders in sales, finance, marketing and service teams to create a coherent customer view. They will:

· Develop and improve the current data architecture using AWS Redshift, AWS S3, AWS Aurora (Postgres) and Hadoop/EMR.
· Improve upon the data ingestion models, ETL jobs, and alarming to maintain data integrity and data availability.
· Stay up-to-date with advances in data persistence and big data technologies and run pilots to design the data architecture to scale with the increased data sets of advertiser experience.
· Partner with analysts across teams such as product management, operations, sales, finance, marketing and engineering to build and verify hypothesis to improve the business performance.
· Manage weekly business reports via dashboards and paper the analyses of daily, weekly, and monthly reporting of performance via Key Performance Indicators.