Global Talent Management (GTM) is a central People eXperience and Technology (PXT) team responsible for creating and evolving Amazon’s human capital and talent products and processes. The GTM Science team is a growing interdisciplinary team within GTM that develops evidence-based products and services that power the growth and development of Amazon’s talent across all of our businesses and locations around the world.
The GTM Science mission is to use data and science to assist and measurably improve every talent decision made at Amazon. We do this by discovering signals in workforce data, integrating those signals and behavioral recommendations into GTM’s consumer-grade products, and helping Amazonians make high-judgement decisions that raise the bar on talent. Our work directly impacts Talent products and programs, including Talent Evaluation, Talent Management, Mobility, and Promotion. Our multi-disciplinary approach covers an array of capabilities, including: data engineering, business intelligence and analytics, research and behavioral sciences, data science, and applied sciences such as economics and machine learning.
GTM Science is looking for a dynamic Senior Manager of Data Engineering to lead a team of Data Engineers building the next generation foundational data infrastructure. This role will deliver and execute the vision for the Data Engineering team including a secure, scalable, efficient technical architecture – built using AWS service such as Lake Formation, S3, Glue, Redshift, Athena, QuickSight, and Sagemaker – to support GTM Science and a diverse set of PXT data teams globally across all Amazon businesses. The leader will also be responsible for data governance of sensitive people data including diversity and inclusion, employee performance among others.
· Define and deliver strategy (3-year plan) for the GTM Science Data Engineering team.
· Partner with PXT Central Technology teams to leverage ‘core and common’ platforms and tools
· Partner with GTM Technology teams on requirements for infrastructure enhancements to scale GTM Science products and services.
· Hire, manage, coach and lead a high performing team of Data Engineers.
· Manage GTM “golden source” data tables and pipelines and establish data governance mechanisms for security, compliance (e.g., GDPR, privacy, depersonalization, retention), availability, quality, and usage of data assets.
· Build a robust data catalog for sharing data assets between producers and consumers.
· Drive engineering operational excellence best practice and metrics.
· Bachelor's degree in a quantitative field such as math, computer science, engineering, finance, statistics.
· 10+ years in a Software or Data Engineering role including people management.
· Broad understanding of enterprise-scale systems and technologies used in data infrastructures. Able to apply technical knowledge to invent, evolve, improve, simplify, etc.
· Drive large-scale data engineering efforts that solve significantly complex or endemic problems for customers, business, or technology teams.
· Identify and solve gaps/opportunities within or between regions, architectures, and organizations (e.g., services, workflows, tooling).
· Decompose complex processes into straight-forward solutions.
· Bring strong, data-driven business and technical judgment to decisions. Make a case for engineering effort resourcing and technology priorities. Determine when to make a case for resourcing (and when not to). Negotiate boundaries for areas of ownership.
· Drive large architecture or organization changes to enable teams to work independently and/or achieve a significant efficiency improvement.
· Handle critical problems, decisions, and escalations. Mitigate risks created by complexity.
· Master’s degree in a quantitative field such as math, computer science, engineering, finance, statistics.
· Experience with AWS data lake and analytics service such as Lake Formation, S3, Glue, Redshift, Athena, QuickSight, Sagemaker, EMR, EC2, Lambda, IAM, VPC, etc.
· Experience working in science organizations in partnership with scientists and software engineers.
· Experience with Agile software development methodologies and framework.
· Project management experience, working with technology and product managers to drive technology roadmaps.
· Experience working with highly confidential people data including diversity and inclusion, employee performance.