Software Engineer – Data Infrastructure

Location

New York, NY

Fully on-site, five days per week.

Company Stage of Funding

Early-stage startup

Founded: 2025

Total Funding: $2M

Team Size: Approximately 4 people across engineering and design.

Office Type

Fully on-site in New York City, five days per week. This is a small founding team environment with lightweight processes, occasional planning sessions, and substantial independent coding time.

Salary

$165,000–$290,000 base salary, depending on experience.

Equity

Competitive equity compensation.

Visa

Candidates must have valid US work authorization. New visa sponsorship is not available. Visa transfers may potentially be considered for exceptional candidates.

Employment Type

Full-time

Company Description

This company is building a modern data infrastructure platform for alternative investment managers, including private equity firms, hedge funds, and other asset managers. As private markets continue to grow, investment firms need better ways to consolidate information from fragmented systems, spreadsheets, financial records, documents, and third-party tools.

The platform ingests data from disparate sources and transforms it into structured, typed, and related records with permissions and full data lineage. It also provisions and manages dedicated AWS infrastructure for customers, providing a governed data foundation on which applications and AI-powered workflows can operate.

The product is designed to replace spreadsheets and legacy investment operations tools with modern, scalable infrastructure. It brings sophisticated data capabilities traditionally associated with large financial institutions to a broader market of private investment managers.

The company has raised approximately $2M and is backed by investors including Primary Ventures, Inverted Capital, and industry leaders with experience at major investment firms and financial data companies. The founding team brings expertise from fintech, investing, Microsoft, Goldman Sachs, and high-growth startups.

The platform is AI-native, using LLMs across data extraction, transformation, and structuring workflows. The company also works directly with asset managers and their portfolio companies to establish customized data infrastructure and technical roadmaps.

The Software Engineer – Data Infrastructure role is designed for engineers who want to build foundational systems from scratch in a very small, high-caliber team. Engineers work across the data infrastructure stack, own projects end to end, and contribute directly to architectural decisions.

The culture emphasizes autonomy, engineering craft, speed, and comfort with ambiguity. Rather than following extensive processes or waiting for detailed instructions, engineers receive broad areas of ownership and are expected to make progress independently over extended periods. The team also uses internal AI tooling and parallel agents to accelerate development.

What You Will Do

1. Build Core Data Infrastructure

• Design and ship production systems for data ingestion, structured context, and the governed data foundation that applications rely on.

• Build infrastructure that consolidates information from documents, spreadsheets, meetings, financial systems, CRMs, and third-party tools.

• Transform diverse data formats into structured, typed, and related records.

• Develop systems that preserve data lineage, permissions, and relationships across information sources.

• Create foundational infrastructure that supports reliable downstream applications and AI-powered workflows.

• Make architectural decisions that shape the platform as it evolves.

2. Build and Manage Customer-Specific AWS Infrastructure

• Design systems that provision and manage full AWS clusters on behalf of customers.

• Build infrastructure workflows that support isolated, customer-specific environments.

• Automate infrastructure provisioning, configuration, and lifecycle management.

• Use AWS and infrastructure-as-code tooling to make deployments repeatable and reliable.

• Ensure customer environments support the platform's data, security, and application requirements.

• Develop scalable infrastructure patterns that can support additional customers as the business grows.

3. Develop Data Ingestion and Transformation Systems

• Build pipelines that ingest data from a variety of financial and operational systems.

• Design reliable workflows for extracting, transforming, validating, and structuring information.

• Work with structured and unstructured data from documents, spreadsheets, ledgers, and third-party applications.

• Develop typed data models and relationships that make information usable across the platform.

• Implement workflows that preserve data quality, traceability, and access permissions.

• Identify opportunities to improve ingestion reliability, processing speed, and maintainability.

4. Build AI-Native Data Workflows

• Use LLMs extensively throughout the engineering workflow and product development process.

• Build infrastructure that supports AI-driven data extraction, transformation, and structuring.

• Integrate model-powered workflows into reliable production systems.

• Leverage internal AI infrastructure, parallel agents, and containerized development environments to increase engineering velocity.

• Explore ways to automate complex data workflows using LLMs.

• Ensure AI-powered processing produces structured, traceable, and useful outputs.

5. Own Engineering Problems End to End

• Take ambiguous technical briefs and turn them into production systems that customers rely on daily.

• Own projects from problem definition and architectural design through implementation, deployment, and iteration.

• Make progress independently with minimal hand-holding.

• Identify technical risks, evaluate trade-offs, and determine practical implementation approaches.

• Work through unfamiliar problems without relying on extensive documentation or predefined processes.

• Take responsibility for the quality and reliability of the systems you build.

6. Shape Green-Field Architecture and Technical Direction

• Build new systems and infrastructure rather than working exclusively on established legacy codebases.

• Contribute to architecture decisions across the data infrastructure stack.

• Evaluate technical approaches and choose solutions that balance speed, reliability, scalability, and maintainability.

• Develop reusable patterns and abstractions that support future product capabilities.

• Collaborate with the founding team to determine how the platform should evolve.

• Help establish engineering practices as the team and product grow.

7. Work Across the Full Data Infrastructure Stack

• Contribute to backend services, APIs, data pipelines, cloud infrastructure, and supporting application components.

• Use Python and FastAPI to build backend services and data workflows.

• Work with Temporal, dbt, Terraform, AWS, Docker, and related infrastructure technologies.

• Collaborate across the stack rather than being restricted to a narrowly defined engineering function.

• Help solve problems that span application code, infrastructure, and data processing.

• Build practical solutions that serve real customer requirements.

8. Collaborate in a Lean, High-Autonomy Team

• Work closely with the founding team and other engineers on high-impact technical problems.

• Participate in lightweight standups and occasional planning sessions.

• Communicate progress and technical decisions without relying on extensive project management processes.

• Seek feedback when useful while maintaining independent ownership.

• Contribute to a culture of high standards, strong engineering craft, and continuous improvement.

• Help build the company's engineering culture as the team grows.

Ideal Candidate Background

Experience Requirements

• Approximately 1–8 years of software engineering experience, with a strong focus on data infrastructure and Python.

• The role description also references a target of 1–6 years of experience; prioritize candidates based on demonstrated engineering ability and scope of ownership.

• Strong software engineering fundamentals and experience building production systems.

• Experience developing backend services, data pipelines, infrastructure, or platform components.

• Ability to own projects end to end with limited supervision.

• Comfortable working in an early-stage startup with minimal process and changing requirements.

• Interest in building green-field systems and shaping technical direction.

• Demonstrated ability to work independently and make sound technical decisions.

Education Requirements

• No specific degree or university requirement has been provided.

• Prioritize practical engineering ability, production experience, technical judgment, and evidence of ownership over educational credentials alone.

Python and Backend Engineering

• Strong Python proficiency is a core requirement for the role.

• Experience building backend services and production APIs.

• Familiarity with FastAPI or comparable Python frameworks.

• Ability to write maintainable, testable, and reliable code.

• Experience designing service boundaries, handling errors, and debugging production systems.

• Understanding of backend architecture and how services interact with databases, infrastructure, and external systems.

• Ability to learn unfamiliar technologies quickly.

Data Infrastructure and Pipelines

• Experience building data ingestion, transformation, or processing systems.

• Understanding of how to work with structured and unstructured data.

• Familiarity with data modeling, schemas, and relationships between records.

• Ability to build reliable workflows that handle varied data sources.

• Experience with data validation, traceability, and data quality.

• Familiarity with dbt or similar data transformation tools is valuable.

• Experience with workflow orchestration tools such as Temporal is a plus.

Cloud Infrastructure and DevOps

• Experience working with AWS or another major cloud platform.

• Familiarity with infrastructure-as-code tools such as Terraform.

• Experience with Docker and containerized development or deployment environments.

• Understanding of cloud infrastructure provisioning and configuration.

• Ability to build or maintain reliable deployment workflows.

• Familiarity with CI/CD, monitoring, and production operations is beneficial.

• Experience managing isolated or customer-specific cloud environments is a strong advantage.

AI Fluency and Engineering Productivity

• Demonstrated willingness to use LLMs extensively in day-to-day engineering.

• Interest in AI-assisted software development and internal engineering tools.

• Ability to use AI tools to accelerate coding, debugging, and technical exploration.

• Comfort experimenting with multiple AI agents and containerized development environments.

• Interest in building AI-native products and data workflows.

• Ability to evaluate AI-generated output critically and maintain high engineering standards.

Ownership and Problem-Solving

• High autonomy and a strong bias toward action.

• Comfortable translating ambiguous briefs into working software.

• Ability to own technical problems from discovery through production deployment.

• Strong judgment when deciding how to approach unfamiliar problems.

• Willingness to make progress without detailed specifications.

• Ability to balance speed with reliability and maintainability.

• Enjoys building systems from scratch and solving foundational engineering problems.

• Takes responsibility for outcomes rather than waiting for direction.

Startup and Culture Fit

• Comfortable working in a very small team with significant individual impact.

• Enjoys long stretches of focused, independent engineering work.

• Can collaborate effectively without heavy processes or frequent meetings.

• Motivated by technical craft and building useful software.

• Willing to work across different parts of the stack.

• Comfortable with rapid changes in priorities and evolving product requirements.

• Interested in contributing to architecture and engineering culture.

• Willing to work on-site in New York City five days per week.

Nice-to-Have Qualifications

• Experience with FastAPI, Temporal, dbt, Terraform, AWS, React, or Docker.

• Experience building multi-tenant or customer-specific infrastructure.

• Experience provisioning cloud environments programmatically.

• Familiarity with data lineage, permissions, and governed data platforms.

• Experience ingesting data from financial systems, CRMs, documents, or spreadsheets.

• Experience with AI-powered data extraction and transformation.

• Familiarity with investment management, private equity, hedge funds, or fintech.

• Experience in an early-stage startup or a small engineering team.

• Experience with containerized development environments and AI coding agents.

• Experience designing green-field infrastructure from scratch.

Compensation & Benefits

• Base salary range of $165,000–$290,000, depending on experience.

• Competitive equity compensation.

• Full-time engineering role with direct influence over product and architecture.

• Opportunity to work closely with a founding team with deep fintech and investing expertise.

• High autonomy and ownership in a small engineering organization.

• Significant exposure to green-field infrastructure and AI-native data workflows.

• Opportunity to work on foundational systems for the alternative investment industry.

• Five days per week working on-site in New York City.

Why Join

• Build the modern data infrastructure platform for alternative investment managers.

• Work on green-field systems with direct influence over architecture and technical direction.

• Solve complex problems involving data ingestion, lineage, permissions, and cloud infrastructure.

• Build and manage customer-specific AWS clusters.

• Work in an AI-native engineering environment that uses LLMs extensively.

• Take broad ownership of meaningful engineering projects with minimal bureaucracy.

• Work closely with a founding team that brings experience from fintech, investing, and high-growth startups.

• Join an early-stage company where each engineer has an outsized impact.

• Help replace fragmented spreadsheets and legacy investment operations tools with modern infrastructure.