- 3+ years of experience as a Data Engineer or in a similar role
- Experience with data modeling, data warehousing, and building ETL pipelines
- Experience in SQL
Are you interested in shaping the future of Advertising and B2B Marketing? We are a growing team with an exciting charter and need your passion, innovative thinking, and creativity to help take our products to new heights.
Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of digital advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon owned and operated sites and on other high quality sites across the web. We start with the customer and work backwards in everything we do, including advertising. If you’re interested in joining a rapidly growing team working to build a unique, world-class advertising product with a relentless focus on the customer, you’ve come to the right place.
The future of our business is compelling and we are focused on building a robust, innovative business, enabling advertisers of all sizes. Our advertising products range from eCommerce ads that integrate the power of familiar shopping features to visually stunning Kindle screensavers. We can reach customers on Amazon properties, both web and mobile, and across our ad platform. A strategic part of our charter is to build a portfolio of self-service, cost-per-click advertising programs to enable both large and small advertisers to engage with customers in relevant ways. Core to these programs is our understanding of our advertisers and marketing programs from a robust analytical perspective. Teams are dedicated to diving deep into telemetry and performance data to drive insights and future marketing strategy to grow our self-service advertising programs among advertisers.
As a Data Engineer, you will join the Marketing Science team that is composed of science, engineering, and product functions. Marketing Science empowers Amazon to transform its business-to-business (B2B) marketing and advertiser communication initiatives into smarter programs that are tailored to advertisers’ educative needs. As a contributor to the identification of advertiser insights and recommendations, you will (1) take on projects and make enhancements that improve data processes (e.g., data auditing solutions, management of manually maintained tables, automating, ad-hoc or manual operation steps). (2) build data pipelines to feed machine learning models for real-time and large-scale offline use cases, (3) perform data modeling to support machine learning model training and offline, batch inference workflows, (4) work closely with Data and Applied scientists to scale model training and explore new data sources and model features, and (5) work closely with Software Development Engineers to support offline data needs.
Moreover, you will work on project ideas with customers (e.g., analysts, scientists), stakeholders, and engineer peers. You help balance customer requirements with team requirements and help your team evolve by actively participating in the code review process, design discussions, team planning, and ticket/metric reviews. You focus on operational excellence, constructively identifying problems and proposing solutions and you are able to train new peers about how team data solutions are constructed, how they operate, how secure they are, and how they fit into the bigger picture.
- 3+ years of experience as a Data Engineer, BI Engineer, Business/Financial Analyst or Systems Analyst in a company with large, complex data sources.
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience working with AWS big data technologies (EMR, Redshift, S3)
- Demonstrated strength in data modeling, ETL development, and data warehousing
- Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations