Data Engineer

Kepler: AI you can trust and verify.

Every AI tool has the same flaw: the model touches the data. It guesses numbers, fabricates sources, and gives you a different answer every time you ask. For the people making million-dollar decisions, that's not a feature gap. It's a dealbreaker.

We built the architecture that makes hallucination structurally impossible. AI interprets your intent. Deterministic code retrieves every figure from source documents. The model never produces a number, so it can't get one wrong. Every output traces to a filing, a page, a line item. Every calculation shows its formula. Every answer is defensible.

Live in production. 950K+ SEC filings. 14K+ companies. 40M+ documents. 27 global markets. Trusted by firms that don't get to be wrong.

The architecture is domain-independent. Finance is first because the pain is sharpest. Healthcare, legal, insurance are next. Same system, new data sources. We're not building a finance product. We're building the verification layer for the entire AI stack.

Founded by Vinoo Ganesh (7 yrs Palantir, Head of Business Engineering at Citadel) and Dr. John McRaven (11 yrs Palantir, created the analytics engine behind $100M+ contracts with BP and Airbus, Ph.D. Physics). Backed by the founders of OpenAI, Facebook AI Research, MotherDuck, dbt, and Outerbounds.

You'll build and maintain the data pipelines and infrastructure that power Kepler's AI-driven research platform. Financial data is fragmented and messy: SEC filings, earnings transcripts, market data feeds, research reports, and internal documents. You'll help ingest, structure, and unify all of it into a coherent system where every answer traces back to its source.

This is a greenfield environment with significant opportunity to influence technical direction, establish best practices, and grow alongside the platform.

• Own and ship a major data pipeline end-to-end

• Contribute to foundational technology decisions that shape platform architecture

• Build ingestion systems that power real financial research workflows

• Help establish data engineering patterns and best practices for the team

Build & Maintain Data Pipelines

• Design and implement ingestion pipelines from heterogeneous sources: SEC filings, earnings transcripts, market data, research reports, and internal documents

• Handle structured, unstructured, and semi-structured data formats

• Ensure pipelines are reliable, scalable, and well-monitored

Support Data Architecture

• Contribute to decisions around storage technologies, indexing strategies, and retrieval systems

• Build semantic layers that normalize entities across sources and resolve ambiguity

• Implement data provenance so every number traces to a source document and section

Enable AI & Analytics Workloads

• Build infrastructure for document processing, embedding pipelines, and vector search

• Support retrieval systems that surface the right context from millions of documents

• Collaborate with AI/ML engineers to ensure data infrastructure meets model requirements

Ensure Data Quality & Governance

• Build and maintain observability, monitoring, and validation systems

• Implement data quality frameworks and governance standards

• Own data reliability metrics and drive continuous improvement

Ship with Production Excellence

• Write comprehensive tests and maintain CI/CD deployment pipelines

• Participate in code reviews and contribute to engineering best practices

• Monitor production systems and respond to data quality issues

Required

• 5+ years of data engineering experience building production data pipelines and platforms

• Strong experience designing ingestion, storage, transformation, and retrieval systems

• Proficiency working with structured, unstructured, and semi-structured data

• Hands-on experience with modern data stack tools: orchestration (e.g., Airflow, Temporal), storage (e.g., PostgreSQL, S3), and processing frameworks

• Solid understanding of SQL, Python, and at least one systems language (Rust, Go, etc.)

• Experience with Git workflows, CI/CD, and automated testing

• Strong communication skills, able to articulate technical trade-offs to both engineering and business stakeholders

• Thrives in fast-paced, high-ownership environments

Nice to Have

• Experience with vector databases, embedding pipelines, or retrieval-augmented generation (RAG) systems

• Familiarity with document processing or audio data pipelines

• Financial services or fintech data experience

• Experience with data quality frameworks and governance tooling

• Exposure to Kubernetes, Docker, and infrastructure-as-code (e.g., Pulumi, Terraform)

Don't check every box? Apply anyway. We prioritize speed of learning, problem-solving skills, attention to detail, and drive to build world-class data infrastructure.

• Direct collaboration with founders who built Palantir Foundry and data infrastructure at Citadel

• Weekly 1:1s with founders

• Architectural reviews and guidance on data system design

• Clear growth path toward senior data engineering and platform leadership roles

• Frontend: React, Typescript, Vite, Tailwind, Radix, TanStack, Zustand

• Backend: Rust, Node.js, Python, PostgreSQL, Redis

• AI/ML: OpenAI, Anthropic, MCP SDK,

• Infrastructure: AWS (S3, RDS), Docker, Temporal, Kubernetes, Dataflow

• Tools: Git, GitHub, Pulumi, Auth0, SharePoint
  

• Comprehensive medical, dental, vision, 401k, insurance for employees and dependents

• Automatic coverage for basic life, AD&D, and disability insurance

• Daily lunch in office

• Development environment budget - latest MacBook Pro, multiple monitors, ergonomic setup, and any development tools you need

• Unlimited PTO policy

• "Build anything" budget - dedicated funding for whatever tools, libraries, datasets, or infrastructure you need to solve technical challenges, no questions asked

• Learning budget - attend any conference, course, or program that makes you better at what we're building
  

• Forward-Deployed with Product DNA: We own customer outcomes while building a product company. That means embedding, iterating, and deploying where our customers are. We don't win if they don't win.

• Extreme Ownership: Big vision, shared ownership. If you notice a problem, you own it. Authority comes from initiative, not job titles. Once you step up, you're accountable for the outcome.

• Production-First Engineering: We design for critical workloads from day one. Durable execution, blue/green deploys, automated rollbacks, continuous delivery with end-to-end observability. Every change lands safely and stays resilient under real-world load.

• Trust as the Default: People do their best work when confidence is mutual. We show our work, keep our promises, and flag risks before they bite. Trust isn't an aspiration. It's the baseline.

• Keep Raising the Bar: We block time for training, code-health sprints, and deep-dive tech talks. A sharper team and a cleaner stack pay compounding dividends. Continuous learning isn't a perk. It's part of the job.

Kepler is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We are committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment.