# Principal Engineer, Data Platform at AlphaSense

> Remote · United States · Principal · Data engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Aalphasense%3A8925866002 (read 2026-11-04).

## Facts (observed)

- Company: [AlphaSense](https://alderhq.app/companies/alphasense)
- Location text: Remote - United States; Remote - United States
- Where it can be done from (inferred): Remote · United States (evidence: "Location: Remote - United States; Remote - United States")
- Posted: 2026-10-30 (5 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/alphasense/jobs/8925866002
- Skills listed (inferred): PostgreSQL, MongoDB, Redis, Kafka, AWS, Kubernetes, Docker, Distributed systems

## Pay and sponsorship

- Posted pay: $246k–$339k per year (from the listing's pay field)
- Market band for similar posted roles (United States, n 14): p25 $222k, median $255k, p75 $293k

## Description (the employer's text)

### About AlphaSense

AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of over 500 million premium business documents — including equity research, earnings calls, expert interviews, filings, news, and internal proprietary content. Purpose-built for speed, accuracy, and enterprise-grade security, AlphaSense helps teams extract critical insights, uncover market-moving trends, and automate complex workflows with high-quality outputs. With AI solutions like Generative Search, Generative Grid, and Deep Research, AlphaSense delivers the clarity and depth professionals need to navigate complexity and obtain accurate, real-time information quickly. For more information, visit www.alpha-sense.com. Come join us.

### About the Role

AlphaSense runs a large-scale, Kubernetes-native platform built on a modern microservices architecture, serving the majority of the Fortune 500. We are moving it to a cell-based architecture, where each cell is an independent deployment of the platform.

The hardest part is data. Our platform relies on a broad ecosystem of data platforms and storage systems, including Vespa, MongoDB, ClickHouse, Redis, ScyllaDB, PostgreSQL, Temporal, and Kafka, to name a few. This role is focused on the Data Platform: the capabilities that let engineering teams build, run, and scale their storage at large scale without each team becoming an expert in every system.

You will help set the technical direction for the Data Platform and build it rather than only specify it. A central part of the work is building the capabilities for large-scale data migrations, so we can create, move, split, and rebalance data across cells and geographies as the cell-based architecture rolls out.

We prefer managed solutions over self-hosted ones, and we expect engineers to own their storage. Teams run what they build, and this role makes that model work at scale: you build it, you run it, and you scale it. Your job is to give teams the platform, standards, and tooling to do that safely, and to own the reliability and scalability of the platform itself.

### Responsibilities

- Help set the architecture and roadmap for the Data Platform and the storage ecosystem behind it
- Build the capabilities for large-scale data migrations across cells, regions, and geographies, including tooling for moving, splitting, validating, and rolling back data with minimal downtime
- Enable product teams self-service ways to provision, operate, and scale their own storage
- Drive the data-layer changes that cell-based architecture requires, including data placement, tenant routing, replication, and isolation
- Define and apply the managed-first approach: choose managed services where they fit, and build and run self-managed systems only where there is a clear reason
- Find and close scalability gaps in data stores across regions, cells and deployment models
- Build shared libraries that give product teams self-service storage capabilities
- Closely partner with Cloud Platform, SRE, and DevEx teams
- Raise the architectural bar across the organization and mentor Staff and Senior engineers
- Apply AI tooling to data platform work, such as slow query reviews and incident diagnosis
### Requirements

- Deep hands-on experience designing and operating data platforms and storage systems
- Production large-scale experience with storages (e.g. MongoDB, ClickHouse, Redis, PostgreSQL)
- Cloud-native data storage experience, including managed data services on AWS
- Strong background in Kubernetes and containers
- Track record of designing and running large-scale data migrations, including live migrations
- Experience with cell-based, multi-tenant or multi-region architectures
- Experience building platforms and tooling that other engineers use, and comfort with a model where engineers own their storage and run what they build
- Demonstrated ability to land significant changes in code owned by other teams

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aalphasense%3A8925866002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.