What are the responsibilities and job description for the Data Engineer II, Workforce Intelligence position at Amazon?
DESCRIPTION
At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, innovative and driven leaders. If you'd like to help us build the place to find and buy anything on-line, this is your chance to make history and be intimately involved in growing our business by leading and supporting hourly staffing initiatives.
The vision of Workforce Intelligence is to design the ideal workforce to meet the customer promise anywhere. The Workforce Intelligence team applies data and insights to optimize the experience and operations for Amazon’s largest candidate population – Tier 1 Associates, who bring the magic of Amazon’s industry-leading Customer Fulfillment experience to life. The pace at which job creation and hiring must happen to support the scale and complexity of Amazon businesses is a problem Amazon is uniquely qualified to solve for and continually innovate on. Workforce Staffing (WFS) literally hires by the hundreds of thousands across multiple role times, job attributes, and for a range of business lines.
As a Data Engineer, you will provide technical leadership, lead data engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective. You strive for simplicity, demonstrate creativity and sound judgement. You deliver data solutions that are customer focused, easy to consume and create business impact. You are passionate about working with huge datasets and have experience with the organization and curation of data for analytics. You have a strategic and long term view on architecting advanced data eco systems. You are experienced in building efficient and scalable data services and have the ability to integrate data systems with AWS tools and services to support a variety of customer use cases/applications.
In this role, you have the opportunity to:
- Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science.
- Implement data ingestion routines both real time and batch using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.
- Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.
- Collaborate with engineers to help adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation
- Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service modeling and production support for customers.
Key job responsibilities
As a Data Engineer with Workforce Intelligence, you will partner with Software Engineers, Data Scientists and Business Intelligence Engineers. You will gain a deep understanding of our services and the data they produce, and become our resident expert in how to transform that data into a format that is useful for analytics and business intelligence. You will proactively help to identify new data for integration with our platform, and propose and implement new technologies to help us better understand our data.
In this role, you will serve as the expert in designing, implementing, and operating a stable, scalable, low cost environment to flow information from the source systems to data warehouse into end-user facing reporting applications such as Tableau or AWS QuickSight. Above all, you will bring large datasets together to answer business questions and drive data-driven decision making.
About the team
The Workforce Intelligence team supports the Workforce Staffing organization by transforming data into insights on high volume hiring within Amazon. While WFS is responsible for the management of the Tier 1 talent supply chain, the Workforce Intelligence team in particular focuses on defining analytics that measure the performance of the program. While we continue to work on metrics that help monitor the health of the program we also spend efforts on identifying efficiencies in the process path through the power of data and predictive analytics.
BASIC QUALIFICATIONS
- 3 years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience as a Data Engineer or in a similar role
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)
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- 3 years of SQL experience
- Experience with Airflow, Step functions, any other orchestration frameworks.
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.
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.
