Data Engineer III, Organizational Planning and Effectiveness
2 weeks ago
Key job responsibilities
- Create end-to-end analytics solutions (acquire, integrate, and transform various data, develop production reporting pipelines, develop front-end BI tools (e.g., Tableau, Power-BI, Quicksight).
- Influence organization decisions through providing data-driven recommendations verbally or in writing, as an analytics thought-leader and subject matter expert for your domain
- Work efficiently and routinely deliver the right things with limited guidance in complex and/or ambiguous problem areas in existing or new BI initiatives.
- Take the long-term view of your team's solutions and how they fit into the team’s architecture. Consider where each solution is in its lifecycle and where appropriate, proactively fix architecture deficiencies.
- Take the lead on large projects that require the work of multiple persons. Divide a BI project into parallel work that can be performed by you and other data professionals and then reassembled successfully into a cohesive launch.
- Consider and explain capabilities and limitations of the systems you work with (e.g., cluster size, concurrent users, data classification) to technical and non-technical audiences, helping them understand what’s currently possible and which efforts need a technology investment (e.g., data pipeline doesn’t exist, report needs additional functionality).
- Establish good working relationships with teammates and peers. Recognize discordant views and take part in constructive dialogue to resolve them.
- Influence team’s strategy, taking ownership of team infrastructure, providing a system-wide view and design guidance. - Drive BI engineering best practices (e.g., Operational Excellence, code reviews, syntax and naming convention, metric definitions, alarms) and set standards.
- Work to resolve the root cause(s) of pervasive or persistent problems; your efforts may unblock related teams. This may require you to influence decisions made by other teams
A day in the life
You want to make changes that help Amazon’s most senior leaders build healthy organizations. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in data analytics, reporting, modeling, transformation and warehousing, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem-solving skills. You have great judgment and communication skills, and a history of delivering results. You are considered a technical leader on your team.
About the team
The Organizational Planning and Effectiveness team formed in November 2023 to create a single-threaded focus within Global Talent Management and Compensation. The team owns two enterprise-wide products: Rise, a succession planning product that helps leaders identify talent for critical roles and plan for future talent moves; and Blueprint, an organizational design product in prototype that helps leaders rapidly model alternate scenarios for structures, roles, and reporting relationships. The team is building a new enterprise-wide executive talent management product that helps leaders translate business strategy into executive talent needs, then identify, develop, and match successors to the right roles. This growing team partners with Research Science, UX Design, and additional talent management teams across the company to build innovative experiences for Amazon’s leaders and their HR partners.
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA
BASIC QUALIFICATIONS
- 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
PREFERRED QUALIFICATIONS
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR- Experience operating large data warehouses
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