Strativ Group
Staff Data Engineer (San Francisco)
Strativ Group, San Francisco, California, United States, 94199
San Francisco (Hybrid)
Founding/Staff Data Engineer
$200-300k base
Our client is an
elite applied AI research and product lab
building AI-native systems for financeand pushing frontier models into
real production environments . Their work sits at the intersection of
data, research, and high-stakes financial decision-making .
As the
Founding Data Engineer , you will own the
data platform that powers everything : models, experiments, and user-facing products relied on by demanding financial customers. Youll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one.
What youll do: Design and build the core data platform , ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers
to ship production-grade data and feature pipelines that power cutting-edge models. Establish
data quality, observability, lineage, and reproducibility
across both experimentation and production workloads. Deploy and operate data services
using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make
foundational choices
on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems rewriting pipelines or infrastructure when its the right long-term decision .
Ideal candidate: Have
owned or built high-performance data systems end-to-end , directly supporting production applications and ML models. Are strongest in
backend and data infrastructure , with enough frontend literacy to integrate cleanly with web products when needed. Can
design and evolve backend services and pipelines
(Node.js or Python) to support new product features and research workflows. Are an expert in
at least one statically typed language , with a strong bias toward type safety, correctness, and maintainable systems. Have deployed
data workloads and services using Docker and Kubernetes
on a major cloud provider. Are comfortable making hard calls simplifying, refactoring, or rebuilding legacy pipelines
when quality and scalability demand it. Use AI tools to accelerate your work, but
rigorously review and validate AI-generated code , insisting on sound system design. Thrive in a
high-bar, high-ownership environment
with other exceptional engineers. Love deep technical problems in
data infrastructure, distributed systems, and performance .
Nice to have: Experience working with
financial data
(market, risk, portfolio, transactional, or alternative datasets). Familiarity with
ML infrastructure , such as feature stores, experiment tracking, or model serving systems. Background in a
high-growth startup
or a foundational infrastructure role.
Compensation & setup: Competitive salary and founder-level equity Hybrid
role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact
Our client is an
elite applied AI research and product lab
building AI-native systems for financeand pushing frontier models into
real production environments . Their work sits at the intersection of
data, research, and high-stakes financial decision-making .
As the
Founding Data Engineer , you will own the
data platform that powers everything : models, experiments, and user-facing products relied on by demanding financial customers. Youll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one.
What youll do: Design and build the core data platform , ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers
to ship production-grade data and feature pipelines that power cutting-edge models. Establish
data quality, observability, lineage, and reproducibility
across both experimentation and production workloads. Deploy and operate data services
using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make
foundational choices
on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems rewriting pipelines or infrastructure when its the right long-term decision .
Ideal candidate: Have
owned or built high-performance data systems end-to-end , directly supporting production applications and ML models. Are strongest in
backend and data infrastructure , with enough frontend literacy to integrate cleanly with web products when needed. Can
design and evolve backend services and pipelines
(Node.js or Python) to support new product features and research workflows. Are an expert in
at least one statically typed language , with a strong bias toward type safety, correctness, and maintainable systems. Have deployed
data workloads and services using Docker and Kubernetes
on a major cloud provider. Are comfortable making hard calls simplifying, refactoring, or rebuilding legacy pipelines
when quality and scalability demand it. Use AI tools to accelerate your work, but
rigorously review and validate AI-generated code , insisting on sound system design. Thrive in a
high-bar, high-ownership environment
with other exceptional engineers. Love deep technical problems in
data infrastructure, distributed systems, and performance .
Nice to have: Experience working with
financial data
(market, risk, portfolio, transactional, or alternative datasets). Familiarity with
ML infrastructure , such as feature stores, experiment tracking, or model serving systems. Background in a
high-growth startup
or a foundational infrastructure role.
Compensation & setup: Competitive salary and founder-level equity Hybrid
role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact