Job Description
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Company Overview
A Silicon Valley-based venture capital firm that invests in technology companies across multiple stages, from early-stage ventures through growth. The firm operates across areas including artificial intelligence, healthcare, consumer technology, cryptocurrency, enterprise technology, fintech, gaming, and other emerging technology sectors. Its teams work closely with entrepreneurs and portfolio companies while leveraging technology, data, and operational expertise to support company building and investment activities.
Responsibilities
- Own and advance the Global Partnerships data ecosystem, including core datasets, data pipelines, and supporting systems.
- Serve as a technical and strategic partner to Global Partnerships and adjacent teams on data practices, automation, and scalable process design.
- Design, build, and maintain scalable data infrastructure and reliable pipelines that connect Global Partnerships systems while reducing manual work and errors.
- Maintain high standards of data accuracy and proactively identify, investigate, and resolve discrepancies across different platforms.
- Evaluate and implement tools, integrations, and technologies that improve data visibility, connectivity, and decision-making.
- Identify, pilot, and scale AI tools and agentic workflows that reduce manual work and increase operational efficiency.
- Drive adoption of AI solutions through training, documentation, and change management.
- Develop automation and operational processes that improve data accuracy, reliability, and execution speed.
- Stay current with emerging AI tools and translate relevant technologies into practical improvements for internal and partner-facing workflows.
- Build visualizations and quantitative analyses that provide insights into fund and investment performance, including projected exit scenarios, liquidity considerations, and follow-on decisions.
- Develop strong knowledge of portfolio company information systems and manipulate and analyze data to generate actionable insights.
- Refine and calibrate portfolio performance analyses to maintain their accuracy and relevance.
- Lead complex, cross-functional initiatives with substantial autonomy and collaborate with both technical and business stakeholders.
Qualifications
- 5+ years of professional experience, preferably in data analytics, data engineering, or a related field.
- Bachelor’s degree in Computer Science, Data Science, Economics, Finance, Accounting, or another technical field is preferred but not required.
- Strong attention to detail when working with written content, charts, tables, spreadsheets, and changing data across multiple documents.
- Excellent written communication skills and the ability to communicate complex information clearly.
- Demonstrated experience building and maintaining data workflows, automations, or integrations using Python, JavaScript, or a comparable programming language.
- Proven ability to independently lead ambiguous, cross-functional projects and influence stakeholders across technical and business teams.
- Experience owning or administering complex data pipelines or other critical business systems.
- Strong understanding of data completeness, governance, integration, and quality management.
- Ability to work collaboratively with diverse teams and build effective working relationships.
- Ability to anticipate business needs, take ownership, and design scalable solutions.
- Strong problem-solving skills and the ability to break down complex processes and identify opportunities for high-impact improvements.
- Ability to adapt quickly to changing priorities while maintaining attention to detail and quality.
- Strong sense of urgency and professional responsiveness, particularly when supporting partner-facing activities.
- Ability to ask thoughtful questions upfront, identify potential risks, and prevent downstream issues.
- Willingness to take on a variety of responsibilities and contribute across different areas of the organization.
- Ability to communicate effectively, guide teams through change, and serve as a trusted resource for data practices, systems, and operational efficiency.
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