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Summer Internship - AI‑Native Data Platform Engineering

London, England, United Kingdom

Tower Peak Partners is a private markets investment firm launched in 2024. It is headquartered in London and has offices in New York and Rio de Janeiro. The firm is a collective of highly-skilled and recognisable investment professionals and industry executives from around the world. Tower Peak Partners manages thematic investment funds for institutional investors and principal investments through its own balance sheet. The Firm's investment themes capture opportunities in markets that experience deep structural changes, caused by technological advancements, climate change, socio-economic trends and policy developments.The firm delivers investment strategies to institutional investors around the globe, across private equity, venture capital, real assets and intellectual property asset classes. It has 4 dedicated investment teams, each with an average of 25 years of investment experience in industrial transition, aerospace & defense, media, entertainment & sports, and social impact.

2026 Postgraduate 10-week Internship – AI‑Native Data Platform Engineering

 About the role

As the firm continues to scale, Tower Peak Partners is seeking a hands‑on post‑graduate technology intern to join our data engineering and AI platform team. This is an opportunity to work at the intersection of finance, data, and artificial intelligence, contributing directly to the design, build, and deployment of an AI‑native data platform that powers the firm’s investment analytics and automation workflows.

This Postgraduate Internship is offered in London for a period of 10 weeks. Interns demonstrating strong performance and alignment with the firm’s values may be invited to join the firm on a full-time basis following the internship.

The selection process will consist of one or more interviews with members of the Firm, focused on assessing technical engineering skills, problem-solving ability, motivation, and cultural fit. As part of the process, candidates will be asked to complete a practical exercise designed to evaluate proficiency in programming, data engineering and cloud-based tools, such as Python and SQL, and to prepare a short summary of their approach and findings.

Responsibilities

  • Design, build, and deploy scalable data pipelines that automate ingestion, transformation, and curation of investment datasets.
  • Integrate structured and unstructured data sources (fund data, portfolio company metrics, market intelligence) into a unified Snowflake environment.
  • Implement and optimize compute workflows in Azure, leveraging serverless and containerized architectures.
  • Develop and test agentic AI workflows, enabling intelligent data automation and analytical insight generation.
  • Collaborate with investment, operations, and technology teams to map business processes into data driven solutions.
  • Contribute to documentation, monitoring, and performance optimization throughout the platform lifecycle.

 

Skills and Characteristics 

  • Strong problem-solving ability with a high degree of rigour and attention to detail in data and engineering work.
  • Ability to think systematically about data quality, reliability, and scalability when designing and maintaining pipelines and platforms.
  • Interest in working at the intersection of technology and investing, and in applying AI and data engineering to real-world investment workflows.
  • Self-starter attitude with the ability to work independently and contribute ideas proactively in a fast-paced, entrepreneurial environment.
  • Clear and concise written and verbal communication, with the ability to explain technical approaches and trade-offs to both technical and non-technical stakeholders.
  • Collaborative mindset and interpersonal maturity, enabling seamless coordination within small, high-performing, cross-functional teams.

Qualifications

  • Excellent academic credentials from a leading university, ideally in Computer Science, Data Engineering, Applied AI, or a related quantitative field.
  • Strong programming knowledge, preferably in Python, SQL and familiarity with data pipeline frameworks.
  • Experience or coursework related to cloud data platforms, preferably Azure, and modern data stack principles.
  • Familiarity with private equity, venture capital or financial analytics is an advantage.

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