At Discovered Labs we work with $10M - $50M ARR companies to help them get more leads, users and customers from Google, Bing and AI assistants such as ChatGPT, Claude and Perplexity.

We approach marketing the way engineers approach systems: data in, insights out, feedback loops everywhere. Every decision traces back to measurable outcomes. Every workflow is designed to eliminate manual bottlenecks and compound over time.

High-level overview of our approach:

Data-driven automation: We treat marketing programs like products. We instrument everything, automate the repetitive, and focus human effort on high-leverage problems.
First principles thinking: We don't copy what others do. We understand the underlying mechanics of how search and AI systems work, then build solutions from that foundation.
Full-stack ownership: SEO and AEO rarely work as isolated tasks. We work across the entire funnel and multiple surface areas to ensure we own the outcome and clients win.

The Team

We're a deeply technical team building the SpaceX of the AEO & SEO space. You'll work alongside engineers who have built fraud engines powering Stripe, Plaid, and Coinbase; developed self-driving car systems at Aurora; and conducted AI research at Stanford. We don't have layers of management. You'll work directly with founders who can go deep on architecture, code, and product.

This Role

Own the data infrastructure behind automated reporting, AI visibility monitoring, competitive intelligence, and proactive alerting across a growing multi-tenant client base.

The hard problem is operational complexity, less so petabyte scale volume. Many clients, each with multiple data sources, different schemas, different API rate limits, different failure modes, different freshness requirements. When one breaks, it can't take down everyone else. Fault isolation, graceful degradation, and per-tenant reliability are built in from the start.

This is largely greenfield. You'll be building out monitoring, observability, data quality layers and pipeline orchestration.

You report to the CTO and work closely with product engineers who build the features that consume your data layer. You'll define interfaces and data contracts together. There's no platform team. You own your infrastructure, your CI, and your monitoring.

What You'll Do

Multi-tenant data infrastructure. Ingestion, validation, and transformation across multiple data sources. Fault isolation, schema variation, and graceful upstream failure handling.
Third-party API integration. Most of our data comes from external APIs with their own auth flows, rate limits, pagination quirks, and breaking changes. You'll build robust, resilient connectors that handle all of this gracefully across many client accounts.
Data quality systems. Automated checks on distributions, volumes, null rates, and freshness. Statistical validation, not just schema validation. Bad data doesn't make it downstream.
Data observability. Freshness monitoring, volume anomaly detection, schema drift detection, lineage tracking, blast radius analysis. You know the difference between "the code ran" and "the data is correct."
Alerting design. Not just dashboards. Threshold tuning, noise reduction, avoiding alert fatigue. Mean time to detection is a core metric for this role.
Freshness SLAs. Define them per source, build infrastructure to meet them, alert before they breach.
Event-driven trigger infrastructure. Surface performance changes, quality regressions, and freshness violations as events for downstream systems.
Entity data models. Design schemas for client, competitor, and content entities. Own schema evolution and backward compatibility.
Operational environment. CI/CD, containers, deployment pipelines, credential management. Every deploy passes CI before production.

The Ideal Person for This Role

A builder who ships. You care about getting working systems into production, not endless planning or polish. You've built data infrastructure people actually rely on.
An operator, not just an architect. You don't just design systems, you run them. You find satisfaction in making things reliable, not just making them work once.
An owner. You take responsibility for outcomes, not just tasks. When a pipeline you built breaks at 3am, you fix it and make sure it doesn't break again.
Humble and curious. You acknowledge what you don't know, ask good questions, and genuinely want to learn. You take feedback as a gift, not a threat.
A first-principles thinker. You understand why things work, not just how. You can go five levels deep on schema decisions, validation strategies, and architecture tradeoffs.
Always improving. You're not satisfied with "good enough." You actively seek ways to get better at your craft and make systems better over time.

Requirements

4+ years in data engineering, platform engineering, or infrastructure-heavy backend work.
Python, SQL, pipeline orchestration (Airflow, Dagster, Prefect, or similar).
Event-driven architectures or real-time data processing.
Third-party API integration. You've built resilient connectors against external APIs with rate limits, auth flows, pagination, and breaking changes. Not just calling endpoints, but handling the full operational reality.
Pipeline fundamentals. Idempotent pipelines, backfill strategies, and schema evolution handled gracefully in production.
Data quality systems in production. Automated checks on distributions, volumes, freshness, null rates. Not a one-off notebook.
Data observability. Freshness monitoring, anomaly detection, lineage tracking, blast radius analysis.
Alerting design. Threshold tuning, noise reduction, escalation paths. You've thought about false positives as much as missed detections.
Own your infrastructure. Containers, CI/CD, deployment pipelines, monitoring, credential management. No platform team to hand off to.
Multi-tenant or multi-client data systems. Tenant isolation, per-client configuration, and operational overhead at scale.
APIs or service layers for data exposure. You've built interfaces that other systems consume, not just internal scripts.
Collaborative. You'll work closely with product engineers to define data contracts and interfaces. You communicate tradeoffs clearly in writing. You document decisions, write clear specs, and communicate tradeoffs in writing.

Preferred Qualifications

Experience with marketing or analytics data (GA4, GSC, SEO tools)
Prior experience at a fast-moving startup

What's in It for You

Fully remote position
Work directly with the CTO on high-impact projects
High ownership and autonomy. No micromanagement.
First-hand exposure to cutting-edge AI and search technology
Your work will directly impact well-known (10M+ ARR) companies' performance
Join a fast-growing company at the intersection of AI and marketing

Our Hiring Process

Application
Take-Home Project
Technical Deep Dive
Leadership Interview
Reference Checks

Pls apply here:

tinyurl [dot] com/ysk8w2eu

Senior Data Engineer

Pls apply here:

tinyurl [dot] com/ysk8w2eu

About Discovered Labs

At Discovered Labs we work with $10M - $50M ARR companies to help them get more leads, users and customers from Google, Bing and AI assistants such as ChatGPT, Claude and Perplexity.

High-level overview of our approach:

Data-driven automation: We treat marketing programs like products. We instrument everything, automate the repetitive, and focus human effort on high-leverage problems.
First principles thinking: We don't copy what others do. We understand the underlying mechanics of how search and AI systems work, then build solutions from that foundation.
Full-stack ownership: SEO and AEO rarely work as isolated tasks. We work across the entire funnel and multiple surface areas to ensure we own the outcome and clients win.

The Team

This Role

Own the data infrastructure behind automated reporting, AI visibility monitoring, competitive intelligence, and proactive alerting across a growing multi-tenant client base.

This is largely greenfield. You'll be building out monitoring, observability, data quality layers and pipeline orchestration.

What You'll Do

Multi-tenant data infrastructure. Ingestion, validation, and transformation across multiple data sources. Fault isolation, schema variation, and graceful upstream failure handling.
Third-party API integration. Most of our data comes from external APIs with their own auth flows, rate limits, pagination quirks, and breaking changes. You'll build robust, resilient connectors that handle all of this gracefully across many client accounts.
Data quality systems. Automated checks on distributions, volumes, null rates, and freshness. Statistical validation, not just schema validation. Bad data doesn't make it downstream.
Data observability. Freshness monitoring, volume anomaly detection, schema drift detection, lineage tracking, blast radius analysis. You know the difference between "the code ran" and "the data is correct."
Alerting design. Not just dashboards. Threshold tuning, noise reduction, avoiding alert fatigue. Mean time to detection is a core metric for this role.
Freshness SLAs. Define them per source, build infrastructure to meet them, alert before they breach.
Event-driven trigger infrastructure. Surface performance changes, quality regressions, and freshness violations as events for downstream systems.
Entity data models. Design schemas for client, competitor, and content entities. Own schema evolution and backward compatibility.
Operational environment. CI/CD, containers, deployment pipelines, credential management. Every deploy passes CI before production.

The Ideal Person for This Role

A builder who ships. You care about getting working systems into production, not endless planning or polish. You've built data infrastructure people actually rely on.
An operator, not just an architect. You don't just design systems, you run them. You find satisfaction in making things reliable, not just making them work once.
An owner. You take responsibility for outcomes, not just tasks. When a pipeline you built breaks at 3am, you fix it and make sure it doesn't break again.
Humble and curious. You acknowledge what you don't know, ask good questions, and genuinely want to learn. You take feedback as a gift, not a threat.
A first-principles thinker. You understand why things work, not just how. You can go five levels deep on schema decisions, validation strategies, and architecture tradeoffs.
Always improving. You're not satisfied with "good enough." You actively seek ways to get better at your craft and make systems better over time.

Requirements

4+ years in data engineering, platform engineering, or infrastructure-heavy backend work.
Python, SQL, pipeline orchestration (Airflow, Dagster, Prefect, or similar).
Event-driven architectures or real-time data processing.
Third-party API integration. You've built resilient connectors against external APIs with rate limits, auth flows, pagination, and breaking changes. Not just calling endpoints, but handling the full operational reality.
Pipeline fundamentals. Idempotent pipelines, backfill strategies, and schema evolution handled gracefully in production.
Data quality systems in production. Automated checks on distributions, volumes, freshness, null rates. Not a one-off notebook.
Data observability. Freshness monitoring, anomaly detection, lineage tracking, blast radius analysis.
Alerting design. Threshold tuning, noise reduction, escalation paths. You've thought about false positives as much as missed detections.
Own your infrastructure. Containers, CI/CD, deployment pipelines, monitoring, credential management. No platform team to hand off to.
Multi-tenant or multi-client data systems. Tenant isolation, per-client configuration, and operational overhead at scale.
APIs or service layers for data exposure. You've built interfaces that other systems consume, not just internal scripts.
Collaborative. You'll work closely with product engineers to define data contracts and interfaces. You communicate tradeoffs clearly in writing. You document decisions, write clear specs, and communicate tradeoffs in writing.

Preferred Qualifications

Experience with marketing or analytics data (GA4, GSC, SEO tools)
Prior experience at a fast-moving startup

What's in It for You

Fully remote position
Work directly with the CTO on high-impact projects
High ownership and autonomy. No micromanagement.
First-hand exposure to cutting-edge AI and search technology
Your work will directly impact well-known (10M+ ARR) companies' performance
Join a fast-growing company at the intersection of AI and marketing

Our Hiring Process

Application
Take-Home Project
Technical Deep Dive
Leadership Interview
Reference Checks

Pls apply here:

tinyurl [dot] com/ysk8w2eu

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About Discovered Labs

Founded :

2024

Type :

Products & Services

Size :

0-20

Stage :

Profitable

About

The first AEO/GEO agency that helps B2B companies dominate AI search results. Get discovered by your customers when they ask AI for recommendations.

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