Data Platform Engineer
We're building the ground truth platform for AI.
Generic tools hallucinate data, confabulate reports, and don't show their work. We made accuracy the only possible outcome: every answer traces to its source, every calculation is reproducible, every insight is defensible. We're starting in finance and building the foundational data layer for anywhere decisions depend on trustworthy data.
Kepler was founded by two Palantir veterans (20 years combined) who built core parts of Gotham and Foundry, created Palantir Quiver (the analytics engine behind $100M+ deals with BP and Airbus), led major DoD projects, and served as Head of Business Engineering at Citadel.
Kepler is backed by founders of OpenAI, Facebook AI, MotherDuck, dbt, Outerbounds, and others.
The Role
You'll architect the foundational data platform that powers Kepler's AI research experience. Financial data is fragmented, messy, and comes in every format imaginable: SEC filings, earnings transcripts, market data feeds, research reports, live audio, internal documents. You'll own the architecture that ingests, structures, and unifies all of it into a single coherent system where every answer traces back to its source.
This is a greenfield build. You'll define the storage technologies, search and retrieval systems, indexing strategies, and observability tools that become the foundation for everything we do. You'll drive technical direction, mentor engineers, and make architectural decisions that shape the platform for years to come.
This role is for engineers who want to build the data infrastructure for the AI era, not another dashboard or data warehouse.
Within your first 90 days, you will:
Own and ship a major data pipeline end-to-end
Make foundational technology decisions that shape platform architecture
Build ingestion systems that power real financial research workflows
Establish data engineering patterns and best practices for the team
What You'll Do
Architect the data platform: Define storage technologies, indexing strategies, search and retrieval systems, and observability tools from first principles. Drive technical direction and make high-stakes architecture decisions.
Build ingestion pipelines: Design systems that ingest data from dozens of heterogeneous sources: SEC filings, earnings transcripts, market data, research reports, live audio, internal documents. Structured, unstructured, and everything in between.
Build semantic layers: Create the mapping between raw data and precise definitions that powers our platform. Normalize entities across sources, resolve ambiguity, and ensure the same concept means the same thing everywhere.
Build for AI and analytics: Infrastructure that serves both traditional query performance and AI-native requirements: document processing, embedding pipelines, vector search, retrieval systems that pull the right context from millions of documents in milliseconds.
Build provenance systems: Every number traces to a source document, section, and disclosure. Full lineage that satisfies institutional compliance and makes our AI trustworthy.
Own data quality: Observability, monitoring, validation, and governance. Set the standard for data reliability across the platform.
Mentor and grow the team: Code reviews, architectural guidance, and technical mentorship for engineers.
Ship with production excellence: Comprehensive testing, monitoring, deployment pipelines. Set the bar for engineering quality.
What We're Looking For
10+ years of data engineering experience building enterprise data platforms from scratch
Data architecture: Proven track record designing and scaling ingestion, storage, transformation, and retrieval systems
Diverse data types: Deep experience with structured, unstructured, and semi-structured data. Bonus if you've worked with document processing, audio, or financial data
Modern data stack: Strong opinions about storage technologies, indexing strategies, orchestration tools, and observability
AI infrastructure: Curiosity about vector databases, embedding pipelines, and retrieval systems. You don't need to be an ML engineer, but you want to work at the intersection
Technical leadership: Experience driving architectural decisions and mentoring engineers
Practices: Git workflows, CI/CD, automated testing, data quality frameworks
Systems thinker who cares about how ingestion affects transformation, how transformation affects governance, how governance affects what's possible downstream
Strong communicator who can articulate technical trade-offs to engineering and business stakeholders
Thrives in fast-paced environments with high ownership
Financial services experience preferred but not required
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.
Mentorship & Growth
Direct collaboration with founders who built Palantir Foundry and data infrastructure at Citadel:
Weekly 1:1s with founders
Deep architectural reviews and guidance on data system design
Clear growth path toward staff engineering and leading the data platform team
Shape the data platform that becomes the ground truth for AI
Our Technical Stack
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
Benefits
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
Our Operating Principles
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.