# Principal Architect, Platform & Data Lake

**Company:** [TetraScience](https://hotfix.jobs/companies/tetrascience)
**Location:** Remote
**Role:** Fullstack Engineering
**Experience:** 12+ years
**Skills:** AWS, Kubernetes, ECS, Delta Lake, apache iceberg, SAML, OIDC, RBAC, IAM, search architecture, MLOps, CI/CD, sdk
**Posted:** 2026-07-15

> Own and evolve the core platform architecture for TetraScience's Scientific Data and AI cloud, spanning enterprise platform, scientific search, AI/ML Ops, developer platform, lakehouse, partner integrations, and cloud infrastructure. Requires 12+ years experience with deep ownership of authz, search, data lakes, and AI infrastructure at SaaS scale.

## Job Description

## What you'll own

**Enterprise Platform**: Tenancy, IAM, compliance and admin control plane that enterprise customers use to govern their scientific data environment: SSO/SAML/OIDC, fine-grained RBAC, multi-tenant isolation, UI infrastructure, and tenant onboarding.

**Scientific Search**: Search architecture spanning keyword, semantic, and hybrid retrieval across scientific data, instruments, and metadata: relevance standards, indexing pipeline, and the infrastructure that makes search a reliable product surface.

**AI/ML Ops**: Model serving, agentic infrastructure primitives, embedding services, and the MLOps standards that keep scientific AI outputs traceable and operable under production load.

**Developer Platform**: The internal paved road: CI/CD standards, golden path tooling, SDK design principles, and the adoption metrics that prove it works.

**Developer Productivity**: Developer throughput as a first-class metric: toolchain ownership, local/prod environment parity, and friction reduction from commit to deployment.

**Lakehouse Platform**: Scientific data lake architecture, schema evolution, IDS design standards, and the data access layer that AI workloads and downstream pipelines depend on.

**Partner Integrations**: Integration architecture for lab instrument vendors and AI model partners: reference patterns, security boundaries, and the developer experience that enables self-service onboarding.

**Cloud Infrastructure**: Production architecture, cost governance, and the observability layer from infra signal to customer-visible service health.

## What success looks like in year one

- Authn/Authz architecture is documented, consistent across services, and passing enterprise security reviews without heroics from a single engineer.
- AI/ML infrastructure has a clear architecture and roadmap for MLE inference and training use cases, with strong operational telemetry and cost visibility.
- The developer platform has clear SDKs and a set of standard templates for scientific use cases to start from, with adoption and delivery by multiple scientific use case teams.
- Operational excellence based on a clear O11y architecture rolled out, with every production service having SLOs defined, monitored and managed.
- Cost governance with customer chargeback attribution architecture and operationalized with the finance and field teams.
- Lakehouse platform architecture and operational buildout as a Data Products Platform with strong DX and operational scaling.
- Evolve IDS to open standards based schema and encoding with strongly typed data models and schema-on-write enforcement.
- Published reference architecture for each partner class (lab instrument manufacturers and AI models), with one partner successfully onboarded against each without bespoke engineering support.

## Requirements

- 12+ years in software engineering, with at least 5 at staff or principal level in a SaaS platform or data infrastructure context.
- Deep architecture ownership in at least one of the two fingerprint profiles (Enterprise Data & AI Platforms or Data, Knowledge, and Developer Products), with meaningful range across the other. Coverage of a majority of the eight domains is the bar.
- Demonstrated ownership of enterprise authentication and authorization systems at scale: SAML, OIDC, fine-grained RBAC across a multi-tenant SaaS product.
- Hands-on experience with AI/ML serving infrastructure: built and operated model inference pipelines under production load.
- Search architecture experience: designed and operated a search platform that handles diverse query types (keyword, semantic, or hybrid) across large structured or semi-structured datasets.
- Hands-on experience with data lake architectures at scale: Delta Lake or Apache Iceberg, schema evolution patterns, partition pruning, and the trade-offs between query performance and storage cost.
- Infrastructure fluency on AWS with Kubernetes or ECS. Can read a cost anomaly report, trace it to a root cause, and produce an action within the same week.
- Ability to write and defend architecture decisions: RFCs, trade-off documents, design reviews.
- Strong cross-team communication. Can write a document that produces alignment without a follow-up meeting to explain the document.
- Comfort operating across strategy, architecture, and operations in the same week: setting a multi-year architecture direction and reviewing a runbook gap are both in scope.

## Nice to have

- Experience in regulated industries (biopharma, medtech, financial services) where compliance and data residency are first-class architecture constraints built in from the start.
- Familiarity with scientific data platforms, ELN/LIMS systems, or laboratory informatics ecosystems, including the structural constraints of instrument data.
- Experience designing and operating internal developer platforms as a product: roadmap, adoption metrics, deprecation strategy.
- Experience building partner integration programs at the architecture level: connector SDKs, reference implementations, integration certification criteria, and the developer experience that makes external parties self-sufficient.
- Exposure to lab instrument ecosystems (proprietary data formats, on-prem agent deployment, vendor certification workflows) or analogous hardware-adjacent integration work in medtech or industrial IoT.
- Prior experience as a founding or early platform architect at a Series B–D SaaS company scaling to enterprise.

## Benefits

- Competitive compensation with equity
- Unlimited PTO
- Company-paid Life Insurance, LTD/STD
- 401(k)

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