Data Engineer

Outmarket.AI; United States; On-site; USD 80,000 - 120,000; Full time

Benefits offered by this job

Competitive compensation and meaningful equity

Direct collaboration with founders

Remote-first flexibility

Job summary

Outmarket.AI is seeking a Data Engineer to take ownership of their data infrastructure, which powers analytics and AI products. The role involves building ingestion pipelines, designing data transformations, and ensuring data quality. Candidates should have at least 2 years of experience in production data pipelines, strong SQL and Python skills, and a knack for transforming messy data into reliable insights.

Join a remote-first team and contribute to developing an AI-native product that delivers real impact in the insurance industry.

Qualifications

• 2+ years building production data pipelines with strong SQL and Python.
• Solid relational modeling experience (Postgres) and comfort with large analytical datasets.
• A track record of turning messy, inconsistent source data into reliable product capabilities.

Responsibilities

• Build and operate ingestion pipelines from agency management systems, carriers, and documents.
• Design transformation layers and the models that serve insights and dashboards.
• Orchestrate reliable, observable data jobs and own data quality end to end.

Skills

Strong SQL

Python

Relational modeling (Postgres)

Data pipeline building

Tools

ClickHouse

Temporal

Airflow

Dagster

Job description

ABOUT OUTMARKET

Outmarket is the AI platform for insurance, trusted by more than 250 brokerages to run the work their business depends on. Commercial insurance still runs on dense documents and slow, manual workflows, and that is exactly what we automate: quote comparisons, coverage gap and tower analysis, policy review, and proposal generation, all grounded in our customers’ own data and source‑cited so teams can trust the output.

The impact is concrete. Teams save 12 to 15 hours per person every week, cut errors by roughly 65 percent, and win more business, all on infrastructure that is SOC 2 Type II certified, single-tenant, and never used to train AI models. We are an AI‑first company in both what we build and how we work, shipping quickly and in close partnership with the agencies that rely on us.

WHAT YOU’LL GET

• A high-impact role with ownership from day one.
• Competitive compensation and meaningful equity.
• Direct collaboration with founders and real users.
• Remote‑first flexibility.
• The opportunity to help build an AI‑native product from the ground up.

ABOUT THE ROLE

We are hiring a Data Engineer to own the data plane that powers our analytics and AI products, from ingesting messy, real‑world insurance data to the transformation pipelines and serving layer our customers rely on. You will turn fragmented source‑system data into clean, trustworthy datasets that drive insights and automation.

WHY THIS ROLE

• Own data infrastructure that is core to the product, not a side system.
• Work with genuinely hard, messy, high‑value insurance data.
• Build the pipelines and models that everything analytical and AI‑driven depends on.

WHAT YOU’LL DO

• Build and operate ingestion pipelines from agency management systems, carriers, and documents.
• Design transformation layers (bronze → silver → fact) and the models that serve insights and dashboards.
• Model and tune data across Postgres and ClickHouse for correctness, performance, and cost.
• Define dataset configurations, KPIs, and reconciliation checks that keep numbers trustworthy.
• Orchestrate reliable, observable data jobs and own data quality end to end.

WHAT WE’RE LOOKING FOR

• 2+ years building production data pipelines with strong SQL and Python.
• Solid relational modeling experience (Postgres) and comfort with large analytical datasets.
• A track record of turning messy, inconsistent source data into reliable product capabilities.
• Strong ownership and comfort operating in a fast‑moving environment.

BONUS IF YOU HAVE

• Experience with ClickHouse or another columnar/analytical store.
• Experience with workflow orchestration (e.g., Temporal, Airflow, Dagster).
• Exposure to insurance, fintech, or other document‑and data‑heavy domains.