Data Platform Engineer

About Kepler AI

Kepler AI is building a transparent and intelligent deep-research platform

Financial professionals spend 60-70% of their time manually gathering and consolidating data in a $26.5 trillion industry where speed and accuracy directly impact outcomes. The research landscape has fragmented into dozens of specialized systems — analysts toggle between platforms for financials, transcripts, market data, and macro indicators, reviewing hundreds of documents across disconnected sources for a single investment thesis.

Generic AI tools promise efficiency but fail the trust test. They hallucinate data, confabulate reports, and provide insights without showing their work, forcing analysts back into manual verification. In an industry where being wrong costs millions, opacity isn't acceptable.

Kepler solves this by automating research while maintaining the accuracy and traceability financial decisions demand. The result: faster decisions, deeper analysis, and a competitive advantage where synthesizing information more thoroughly than competitors translates directly to performance.

Kepler AI was founded by two Palantir veterans with 20 years of combined experience building core parts of Palantir's Gotham and Foundry Platform. Our founders created Palantir Quiver, the analytics engine behind $100M+ enterprise deals with BP and Airbus, architected core compute and data systems, led major Department of Defense projects, and served as Head of Business Engineering at Citadel.

We're backed by founders of OpenAI, Facebook AI, Mother Duck, DBT, and Outerbounds.

As a Data Engineer , you'll be the architect of the data infrastructure that powers our AI-native research platform. You'll own the pipelines that ingest, transform, and deliver critical financial data, from SEC filings to proprietary vendor feeds. ensuring our platform has the reliable, high-quality data foundation that sophisticated financial research demands.

This role embodies our belief that exceptional AI requires exceptional data. Your pipelines will feed the research workflows of portfolio managers at firms managing billions in assets. Your data quality decisions directly impact million-dollar investment outcomes.

Within your first 90 days, you will:

• Own and optimize our SEC data ingestion pipelines end-to-end

• Build and maintain integrations with key data vendors

• Develop deep expertise in financial data formats, taxonomies, and quality standards

• Ship improvements that measurably increase data freshness and reliability

This is the right role if you want to build the data backbone of the future of financial research, with guidance from engineers who've scaled enterprise data platforms from zero to global adoption.

• Own critical data pipelines: Design, build, and maintain the pipelines that ingest SEC filings (EDGAR), vendor data feeds, and alternative data sources into our platform.

• Ensure data quality at scale: Implement validation, monitoring, and alerting systems that guarantee the accuracy and freshness our clients' research depends on.

• Architect for reliability: Build fault-tolerant, self-healing pipelines that handle the unpredictable nature of external data sources and vendor APIs.

• Optimize performance: Solve complex challenges around data freshness, processing latency, and storage efficiency for large‑scale financial datasets.

• Drive data infrastructure innovation: Identify opportunities to expand our data coverage, improve pipeline efficiency, and enhance data accessibility for our AI platform.

• Collaborate across teams: Work closely with product and AI engineers to ensure our data infrastructure meets the evolving needs of the platform and our clients.

• 3–5 years of data engineering experience with a track record of building and maintaining production data pipelines.

• ETL/ELT expertise: Deep experience designing and operating data ingestion, transformation, and orchestration systems.

• Strong Python skills with experience in data processing frameworks and pipeline orchestration tools (Airflow, Temporal, or similar).

• SQL proficiency: Advanced SQL skills and experience with analytical databases.

• Data…