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 and family offices to discover, model, and manage their private market exposure. 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 modeling relationships among investors, funds, companies, and other financial entities.
- Support vector database integrations for storing embeddings and enabling semantic search and retrieval across AI agents, models, and providers.
- Develop robust ETL/ELT pipelines for ingesting, cleaning, and transforming internal application data and third-party sources.
- Build batch processing and real-time streaming capabilities to support current analytics and recommendations.
- Design scalable and reliable pipelines with appropriate error handling, monitoring, and operational controls.
- Containerize and orchestrate data tools using Docker, Kubernetes, and AWS EKS.
- Implement CI/CD pipelines for data workflows, processing systems, and models.
- Monitor data platform health and performance through alerts and dashboards.
- Troubleshoot and resolve production issues affecting critical data and AI services.
- Evaluate emerging data engineering and AI technologies and recommend improvements to platform architecture.
- Continuously improve data infrastructure, reliability, scalability, and development practices.
Required Qualifications
- 5+ years of hands-on experience in data engineering or a related field.
- Experience designing and building large-scale data pipelines and storage solutions through the full lifecycle from design to production.
- Strong experience with AWS cloud services used for data engineering, including S3, EC2, ECS, EKS, Athena, Redshift, Glue, and Step Functions.
- Experience with infrastructure-as-code technologies such as Terraform or CloudFormation is a plus.
- Strong SQL and relational database design skills.
- Experience designing efficient schemas and optimizing queries and indexes.
- Experience 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.
- Strong programming experience in at least one major data engineering language.
- Proficiency in Python, with experience using pandas or PySpark being valuable.
- Experience with TypeScript or Node.js in data-related applications is beneficial.
- Ability to work across multiple programming languages and technologies.
- Strong understanding of clean, maintainable code and software engineering best practices.
- Understanding of machine learning data requirements, including preparing datasets, feature stores, and integrating model outputs into applications.
- Experience with vector embeddings and vector databases such as PostgreSQL pgvector, Chroma, or Pinecone is highly desirable.
- Familiarity with AI agent and retrieval-augmented generation frameworks such as LangChain or LlamaIndex is valuable.
- An AI-native mindset, including experience with agentic tools, LLM-assisted development, and emerging AI technologies.
- Solid DevOps and DataOps knowledge, including Docker, Kubernetes, and AWS EKS.
- Experience establishing CI/CD pipelines for data pipelines or machine learning models.
- Familiarity with workflow orchestration tools such as Airflow, Prefect, or dbt.
- Strong analytical and problem-solving abilities with excellent attention to data quality and correctness.
- Ability to troubleshoot complex pipeline issues, data discrepancies, and system performance bottlenecks.
- Understanding of security, compliance, auditability, least-privilege access, and data governance when working with sensitive financial information.
- Ability to work effectively with distributed teams and communicate technical concepts clearly.
- Experience mentoring peers and driving technical projects through completion.
- Adaptability, continuous-learning mindset, and willingness to explore new technologies.
- Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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