Position Summary:
We’re looking for a hardworking, motivated Engineering Manager to join our team and help us continue to scale quickly. In this role, you would be expected to manage and grow the Data Engineering Team within the Data Org. This team is responsible for serving internal teams and product functions to help build and manage key datasets to enable analytics. This team will help build the abstractions on top of our primary data sources to enable data scientists and analysts across Robinhood to be able to have trusted datasets. This team will ensure that datasets have strong SLAs to enable better data driven decisions across the entire company. This team will drive better data decision making across Robinhood.
Key Responsibilities:
- Improve the ease of accessing insights from data within Robinhood by providing structured datasets and abstractions over core data.
- Coach and mentor Robinhood’s talented engineers by giving them actionable feedback, setting clear goals, and coordinating project work with other teams and managers
- Work with other engineering managers to refine and improve Robinhood’s processes and systems for product development
- Deliver high-quality, impactful projects in record time with thoughtful planning
- Communicate effectively with other teams to understand and deliver on data pipelines that are well maintained and are high quality
- Apply technical leadership and expertise to both your team and across engineering
Required Education & Experience:
- Ability to drive Data Engineering projects
- 5+ years of experience as a software engineer or data engineer and 2+years of team management experience
- Proven ability to coach junior/senior engineers and interns
- Effective communication skills with other engineering and non-engineering groups
- Ability to make decisions at a fast pace on an environment with a high degree of uncertainty
- Experience in building on top of Spark, Presto, Hive is a plus
- Sharp technical skills and a deep interest in coding and technical details, with a special emphasis in structure of datasets and data quality