Data Engineer
General Atlantic is a global investment leader founded in 1980, serving as a dedicated partner for entrepreneurs and investors building long-term value. The firm identifies talent and technologies with great potential, empowering innovators with patient capital, operational expertise, and a global platform to scale their businesses.
About General Atlantic
General Atlantic operates as one global platform fueling innovation across multiple investment strategies, including Growth Equity, Credit, Energy Transition, and Infrastructure. Through Growth Equity, the firm accelerates category-leading companies by providing strategic counsel and value-add capabilities. Its Credit strategy delivers strategic capital solutions to high-quality businesses at key phases of their lifecycles. The Energy Transition strategy identifies and scales growth companies with net zero solutions, while its Infrastructure strategy, through Actis, invests in long-term equitable growth in critical infrastructure across energy transition, digitization, and supply chain transformation. General Atlantic manages $126B in assets under management and has invested $121B in total capital since inception, with a presence across 21 global locations serving entrepreneurs and growth-stage companies worldwide.
Skills
About the Role
You will join the Data Strategy team as an Associate-level Data Engineer, reporting to the VP, Head of Data Engineering. You'll work closely with senior data engineers, product managers, infrastructure, security, and application development teams to support the design, development, and maintenance of cloud-based data pipelines, integration frameworks, and self-service data platforms. This is a great opportunity if you are technically strong, highly curious, and eager to grow your expertise in modern data engineering tools and best practices while contributing to enterprise-grade data solutions.
Requirements
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
- 2–4 years of experience in data engineering, analytics engineering, or a related technical role
- Exposure to financial services or enterprise data environments is a plus
- Proficiency in Python and SQL
- Hands-on experience with modern data platforms such as Databricks, Snowflake, or similar
- Familiarity with cloud data services (Azure preferred) including data lakes, data warehouses, and orchestration tools
- Experience working with structured and semi-structured data from APIs, SaaS platforms, and databases
- Understanding of ETL / ELT concepts, data modeling, and pipeline monitoring
- Exposure to BI tools such as Power BI or Tableau is a plus
- Strong problem-solving skills and attention to detail
- Ability to learn new technologies quickly and adapt in a fast-paced environment
- Comfortable working collaboratively within cross-functional teams
- Clear written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders
Responsibilities
- 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
- Assist in the development of centralized data services and APIs for 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-native solutions
- Help prepare curated datasets for use in data lakes, enterprise data hubs, and data warehouses
- Support scalable, performant data storage and compute solutions using Databricks and cloud data services
- Enable reliable data access for analytics, reporting, and data science use cases
- Develop and maintain SQL objects, data models, and transformations
- Write clean, maintainable Python code for data processing and orchestration
- Participate in code reviews, testing, and documentation to ensure data quality and reliability
Benefits
- Medical insurance
- Retirement savings contributions
- Mental and physical health resources
- Equal pay program
- Annual discretionary bonuses
- Long-term incentive programs