The Role
The Precompute Platform team is core to achieving Addepar's transition to using next generation tools and processes for data and analytics with a scalable global operating model. The team's mission is to empower analysts, researchers, and other internal teams to performantly generate data and analytics artifacts.
We require a hands-on engineering manager who will bring to bear their expertise, leadership, and platform management skills to enable this important team to continue to fulfill its role.
What You’ll Do
* Work in partnership with product managers and technology partners to identify requirements and priorities, and map out solutions for challenging technology and workflow problems.
* Gain foundational knowledge of core Addepar systems, including the Addepar Data Lakehouse. Use these insights to work with counterparts within the business analysis teams. Drive new and innovative opportunities to improve the Business Analyst experience.
* Reduce complexity through the adoption of strategic data architecture and workflows
* Improve efficiency by transitioning to an engineering focus, through partnership with global operations teams.
* Unlock data value through the enablement of data governance outcomes
Who You Are
* Several years of relevant work experience and education that shows proficiency in people management and product planning for data and analytics in a financial context.
* A hands-on engineering leader with demonstrated experience in vector-based data processing in Python using libraries such as NumPy, PyArrow, and PySpark.
* A confident and positive outlook with low ego; high degree of ingenuity, resourcefulness, and problem-solving skills.
* Comfortable working in a cloud context, with automated infrastructure and service oriented architecture.
* Practical knowledge of agile practices with an outlook that prioritizes experimentation and iteration combined with an ability to guide teams toward activities and processes that facilitate optimal outcomes.
* Experience with Data Governance initiatives and a track-record of improving data outcomes
Our engineering stack has PySpark on Databricks at its core; while experience in these technologies is preferred, there is flexibility to cross-train if coming from another similar context.
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