Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A growing fintech company is transforming private market investing by enabling registered investment advisors (RIAs) and family offices to discover, model, and manage private market investments more seamlessly. Its platform combines curated fund and co-investment opportunities with institutional-grade infrastructure, providing access to opportunities across venture capital, private equity, private credit, and other private asset classes through a data-rich digital experience.
Key Responsibilities
- Build and extend a data lakehouse on AWS, combining data lake storage and warehouse technologies to manage diverse financial datasets.
- Contribute to a knowledge graph that models relationships among investors, funds, companies, and other key financial entities.
- Support vector database integrations that store embeddings for semantic search and retrieval across AI agents, models, and providers.
- Design and develop robust ETL/ELT pipelines for internal application data and third-party APIs.
- Implement both batch processing and real-time data streaming to support current analytics and recommendations.
Required Qualifications
- 5+ years of hands-on experience in data engineering or related fields.
- Proven experience designing and building large-scale data pipelines and storage solutions through the full development lifecycle.
- Strong proficiency with AWS cloud services, including S3, EC2, ECS, EKS, Athena, Redshift, Glue, and Step Functions.
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation is a plus.
- Strong SQL and relational database design skills.
- Experience designing efficient schemas and optimizing queries and indexes for performance.
- Experience working with data warehouses or lakehouses such as Snowflake or Databricks Delta Lake.
- Familiarity with graph databases such as Neo4j or AWS Neptune and knowledge graph schemas is beneficial.
- Fluency in at least one major programming language used in data engineering, particularly Python.
- Experience with pandas, PySpark, TypeScript, or Node.js is valuable.
- Ability to work across multiple programming languages and integrate data services with backend platforms.
- Strong understanding of clean, maintainable code and software engineering best practices.
- Familiarity with machine learning data requirements, including training datasets, feature stores, and ML model integration.
- Experience with vector embeddings and vector databases such as PostgreSQL with pgvector, Chroma, or Pinecone is a strong plus.
- Familiarity with AI agent and retrieval-augmented generation frameworks such as LangChain or LlamaIndex is beneficial.
- An AI-native mindset with experience using agentic tools, LLM-assisted development, and emerging AI technologies.
- Solid DevOps and DataOps knowledge, including Docker, Kubernetes, and AWS EKS.
- Experience implementing CI/CD pipelines for data applications or machine learning models.
- Familiarity with workflow orchestration tools such as Airflow, Prefect, or dbt is beneficial.
- Strong analytical and problem-solving skills with the ability to debug complex data issues and optimize system performance.
- Strong attention to data accuracy and the ability to identify discrepancies and processing bottlenecks.
- Understanding of compliance and security requirements when handling sensitive investor and financial data.
- Experience applying auditability, least-privilege access, data governance, and security principles in regulated environments.
- Excellent communication and collaboration skills.
- Ability to work effectively with distributed teams and clearly communicate technical concepts.
- Experience mentoring peers and driving technical initiatives through completion.
- Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
Disclaimer
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