Title: Data & Storage Systems Engineer

Salary: $200K–$270K base + competitive equity package with cash/equity tradeoff options

Location: Remote-first, US-based | Hubs in Boston, San Francisco, and Denver | Quarterly Boston team meetups

Visa sponsorship: Not available

Company Description:

Sequoia-backed Series A AI startup building the memory layer for customer-facing teams. $20M+ raised from top-tier investors, founded by former CRM operators who helped build and scale one of the only serious Salesforce challengers.

Job Description

Join a remote-first AI startup building the data foundation for the next generation of customer-facing software.

This company is building an AI-native CRM and memory layer that automatically ingests every call, email, thread, and customer interaction, then makes that context available to the agents and workflows teams build on top. Instead of agents starting cold, the platform gives them persistent memory across accounts, relationships, conversations, and customer history.

The company recently raised a Series A led by Sequoia and is now scaling from early product traction into a much larger platform. The team is small, flat, and highly technical, with proven operators who previously helped build and scale a major CRM platform.

As a Data & Storage Systems Engineer, you will own core parts of the data platform across ingestion, storage, and serving systems. This is deep distributed systems work with direct product impact: replacing and scaling Postgres-backed storage, designing high-throughput transactional systems, building platform APIs and SDKs, improving reliability, and helping the company evolve toward distributed databases, stream processing, OLAP/search systems, and more flexible customer data models.

This is not an infra-only role. The ideal person combines serious backend/distributed systems depth with a product and customer mindset. You should care about architectural elegance, but you should care even more about whether the system creates a better customer experience.

Why this role is remarkable

• Build the data foundation of an AI-native CRM from scratch, working on greenfield distributed systems problems across OLTP storage, ingestion, serving, indexing, permissions, and data access patterns
• Join a Sequoia-backed Series A company at an inflection point, with $20M+ raised from elite investors and a founding team that has already helped scale a major CRM platform before
• Own entire platform areas end to end in a small, flat engineering team where you will make real architectural decisions without bureaucracy, approval chains, or roadmap theater
• Work on hard technical problems including property-graph-style data models, fine-grained permissioning, data lineage, per-tenant schemas, high write throughput, zero-downtime migrations, and multi-access-pattern indexing
• Stay close to the product and customer experience, building infrastructure that directly shapes how users and AI agents interact with customer data
• Work remotely across the US, with hubs in Boston, San Francisco, and Denver, quarterly Boston meetups, unmeasured time off, and a parent-friendly culture that values kindness alongside ambition

What you will do

• Own meaningful parts of the data platform end to end across ingestion, storage, and serving systems
• Evolve core transactional and OLTP storage systems to support significantly higher throughput, stronger reliability, and more flexible data access patterns
• Build and operate distributed app-storage and serving systems using technologies such as TypeScript, Node.js, Kotlin, Rust, Aurora Postgres, FoundationDB, Cassandra, Vitess, DynamoDB-style systems, and related infrastructure
• Design high-leverage platform APIs and SDKs that pull common data access patterns out of application code and accelerate the rest of the engineering team
• Build stream processing pipelines for new data sources and support Kafka-based systems where they connect to core storage and serving needs
• Improve operational excellence by removing failure modes, investing in deployment automation, supporting zero-downtime migrations, and making pager ownership uneventful without a dedicated SRE team
• Help expand the platform to support new customer segments and use cases, including finer-grained permissions, new data types, new ingestion sources, and more advanced indexing and retrieval patterns
• Use AI tools as part of your engineering workflow, delegating implementation details where useful so you can spend more energy on architecture, design, and creative problem-solving

The ideal candidate

• 4+ years of backend or distributed systems engineering experience, with hands-on ownership of data-intensive systems at meaningful scale
• Built and operated transactional/OLTP app-storage or serving systems in production, using technologies such as DynamoDB, Aurora, Vitess, FoundationDB, Cassandra, or similar systems
• Strong in at least one backend language such as TypeScript/Node.js, Kotlin, Java, or Rust
• Experience with infrastructure-as-code and container orchestration, such as Terraform, Kubernetes, ECS, Docker, Pulumi, or similar tools
• Has worked in data-intensive environments with high throughput, reliability requirements, or complex migration challenges
• Ideally has startup experience during a hyper-growth phase, especially Series A through Series C
• Product- and customer-oriented, with the ability to frame infrastructure decisions around business outcomes and user experience, not just technical purity
• Comfortable in a small, flat, high-ownership engineering team where there is no dedicated layer of process between you and the problem
• Bonus points for exposure to OLAP, warehouse, search, or streaming systems such as Kafka, ClickHouse, Elasticsearch, Apache Iceberg, turbopuffer, or similar technologies

This role is not for you if

• You are looking primarily for a management or leadership title rather than hands-on engineering ownership
• Your background is purely data engineering, warehouse, streaming, or infrastructure without core app-storage or serving-system ownership
• You prefer highly scoped tickets, narrow ownership, or large-company specialization
• You are excited by systems elegance in isolation but do not want to think about customer impact, product tradeoffs, or business outcomes

Interview process:

• 45-minute intro call with the Head of Engineering
• 2–4 hour backend take-home coding exercise
• 30–45 minute exercise review and technical conversation
• Final round with 2–4 team conversations, typically covering technical depth, product thinking, collaboration style, and team fit

Next steps:

1. Apply via this LinkedIn job post
2. We will review and reach out if there is a strong match
3. If aligned, we will introduce you directly to the team
4. If this role is not the right fit, we may suggest and make introductions to other high-signal startup roles we are actively recruiting for, always with your permission

A quick note on authenticity:

This is a real, active role we are supporting in close partnership with the hiring team. We do not post speculative roles and work closely with teams on their actual hiring needs