Data Engineering Lead at Ascend

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Apr, 26

Salary

0.0

Posted On

06 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Data Architecture, Data Modeling, ETL, Data Quality, Observability, Financial Metrics, Automation, CI/CD, Mentoring, Stakeholder Management, Technical Standards, Data Pipelines, Analytics Infrastructure, Software Development, Business Logic

Industry

Insurance

Description

What We Do Ascend is transforming the $10 trillion global insurance industry by combining AI automation with embedded fintech. While insurance represents 7.2% of global GDP, outdated financial infrastructure like PDFs, emails, and manual reconciliation continues to slow the industry down. Ascend is the first and market-leading finance automation platform built specifically for insurance, driving the industry’s rapid digital transformation. In just three years: Trusted by thousands of insurance businesses nationwide Processes billions in transactions annually, growing 200%+ per year Built a world-class team of 50+ across San Francisco and Columbus How We Do It Ascend automates the entire insurance financial lifecycle—from collections and accounting to reconciliations and disbursements—helping businesses streamline operations and increase profitability. In the AI era, software alone is no longer enough. For decades, software costs have declined, and with AI, they’re now in free fall. As both software buyers and builders, we fully embrace this shift—it makes technology more powerful and accessible. However, businesses must also maximize monetization, and the future belongs to those that embed themselves into financial flows rather than simply selling SaaS. That’s why AI + fintech is our edge—seamlessly integrating financial automation into transactions to create real, lasting value. Customers don’t just buy software; they unlock powerful financial efficiencies with a 5–7x ROI. Why We Do It Insurance is a $10 trillion-plus industry running on legacy systems. AI alone won’t fix it, but AI-powered automation embedded into payments and transactions will. Ascend’s mission is to become the leading financial automation platform for insurance by 2030—powering the entire industry’s financial operations while transforming the way trillions of dollars move. Your role We are hiring a technically rigorous, systems minded Data Engineering Lead to architect and own the data infrastructure that powers decision making across Ascend. Reporting to the Head of Finance & Data, this role is responsible for the full lifecycle of our data platform, from ingestion of raw, high-volume transactional data to the modeling and orchestration layers that define our most critical business metrics. You are not building dashboards for the sake of dashboards. You are building trusted, scalable data products that allow Ascend to move billions of dollars with confidence as we scale. Responsibilities will include Objective #1: In your first 30 days, you will: Develop a deep understanding of Ascend’s data sources, existing pipelines, and downstream use cases across Finance, Product, and Engineering Build strong working relationships with key stakeholders to understand current pain points, risk areas, and future data needs Evaluate the current data stack and workflows, identifying gaps in reliability, observability, and scalability Begin defining the architectural vision and technical standards for a modern, high-integrity data platform Objective #2: In your first 60 days, you will: Design and begin implementing core components of the Modern Data Stack, including ingestion, transformation, and orchestration Establish data modeling standards using dbt, with clear, version-controlled definitions for core financial and operational metrics Introduce foundational data quality and observability frameworks, including automated testing for freshness, schema evolution, and reconciliation Partner closely with Product Engineering to improve production data exports and ensure analytics infrastructure scales alongside product development Objective #3: In your first 90 days, and beyond, you will: Own and operate a trusted Single Source of Truth that defines Ascend’s core KPIs at the infrastructure level Run scalable, production-grade data pipelines that support high-volume financial transactions with accuracy and auditability Act as the technical authority on data architecture and tooling, evaluating and evolving orchestration, governance, and reverse ETL solutions Mentor future data hires and establish best practices across data modeling, CI/CD, documentation, and peer review Translate complex financial and business logic into reliable, performant data structures that enable confident decision making across the company Benefits Competitive base salary and meaningful equity in a fast-growing company 100% health premiums covered for you and your dependents 401k with employer matching options Unlimited PTO Monthly company-paid commuter benefit options Parental and family leave Lunch provided when in office Dog-friendly office! Join Ascend and be part of our mission to revolutionize the insurance industry and contribute to global financial modernization. Your leadership can make a significant impact on improving the $1T+ insurance industry. Equal Opportunity We are committed to equal opportunity employment and embrace diversity within our organization. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We encourage candidates to apply even if their experience doesn't precisely match this opportunity. We value diversity and are dedicated to equal opportunity employment.

Responsibilities

The Data Engineering Lead will architect and manage the data infrastructure that supports decision-making across Ascend. Responsibilities include developing a deep understanding of data sources, designing core components of the data stack, and ensuring the accuracy and auditability of data pipelines.