Data Engineer
General Atlantic
| Company | General Atlantic |
| Category | Engineering |
| Location | London - More London |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 6 Feb 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
About General Atlantic
General Atlantic is a leading global investor with more than four and a half decades of experience providing capital and strategic support for over 885 companies throughout its history. Established in 1980, General Atlantic continues to be a dedicated partner to visionary founders and investors seeking to build dynamic businesses and create lasting value. Guided by the conviction that entrepreneurs can be incredible agents of transformational change, the firm combines a collaborative global approach, sector specific expertise, a long-term investment horizon, and a deep understanding of growth drivers to partner with and scale innovative businesses around the world. The firm leverages its patient capital, operational expertise, and global platform to support a diversified investment platform spanning Growth Equity, Infrastructure, and Strategic Solutions. General Atlantic manages approximately $126 billion in assets under management, inclusive of all strategies, as of March 31, 2026, with more than 900 professionals in 20 countries across five regions. For more information on General Atlantic, please visit www.generalatlantic.com. About Actis
In October 2024, General Atlantic acquired Actis, which now operates as its Sustainable Infrastructure business alongside its existing strategies in Growth Equity, Credit, and Climate. Actis is a leading global investor in sustainable infrastructure, delivering competitive returns for institutional investors and measurable positive impact for the countries, cities and communities in which it operates. Actis invests in structural themes that support long-term, equitable growth in defensive, critical infrastructure across energy transition, digitalization transition, and supply chain transformation. The firm’s decades of global experience, operational know-how and strong culture allows it to create global sustainability leaders at scale. Since inception, Actis has raised $26 billion to invest in a better tomorrow. Actis is a signatory to the United Nations backed Principles for Responsible Investment (UNPRI), an investor initiative developed by the UNEP FI. The firm has consistently been awarded the highest rating score in the UNPRI independent assessment. You can learn more about Actis at www.act.is .
Key Purpose of Role
General Atlantic is seeking an Associate-level Data Engineer to join the Data Strategy team. This role will report to the VP, Head of Data Engineering, and will work closely with senior data engineers, product managers, infrastructure, security, and application development teams.
This role will support the design, development, and maintenance of cloud-based data pipelines, integration frameworks, and self-service data platforms. This role is ideal for a technically strong, highly curious engineer who is eager to grow their expertise in modern data engineering tools and best practices while contributing to enterprise-grade data solutions.
Key Responsibilities (this list is not exhaustive)
Data Integration & Preparation
Support the design, development, and maintenance of cloud-based data ingestion and integration pipelines
Build and maintain ETL / ELT workflows using Python, SQL, Spark, and Databricks
Assist in integrating data from heterogeneous sources including SaaS platforms, APIs, databases, and cloud applications
Contribute to the development of reusable data integration components and frameworks
Monitor data pipelines, troubleshoot issues, and support production operations
Data as a Service
Assist in the development of centralized data services and APIs that enable downstream consumption by analytics, reporting, and application teams
Support the creation and maintenance of logical data models and service-layer abstractions
Participate in building batch and near-real-time data processing workflows
Contribute to modernization initiatives migrating legacy data processes to cloud-n