Life insurance is one of the world's most important products. It's also one of the hardest to build, distribute, and modernize. Bestow exists to change that.
Bestow is a leading vertical technology platform serving some of the largest and most innovative life insurers. Our platform unifies the fragmented, legacy value chain, enabling carriers to launch products in weeks instead of years. Carriers choose us to scale and operate at unprecedented speed, powered by AI and automation.
Bestow isn't selling policies. We're building the infrastructure that helps an entire industry move faster, reach more people, and deliver on its promise.
Backed by leading investors (Goldman Sachs, Hedosophia, NEA, Valar, 8VC) and trusted by major carriers, Bestow is powered by a team that moves with precision, purpose, and heart. If you want to help reimagine a centuries-old industry with lasting impact, join us.
Bestow offers flexible remote/hybrid work, meaningful benefits, equity, and substantial growth opportunities.
Bestow participates in the E-Verify Program.
The Bestow Data Engineering team plays a significant role within the organization, working across the entire company to provide scalable data solutions within the platform and toward integrations with external partners. The data engineering team works closely with internal analytics and data science team members in order to improve data architecture, rapidly prototype, and serve data science predictions. In addition, data engineers work closely with stakeholders, members from product, and engineering to design and launch new systems for extracting, transforming and storing data. You’ll be called upon to improve Bestow’s data reliability, efficiency and quality and will be expected to scale your solutions to the cloud environment of a SaaS company, iterate quickly, and make pragmatic choices around what tools and technologies to adopt.
As a Staff Data Engineer, you will serve as a technical leader and strategic advisor for Bestow's data infrastructure and architecture. You will drive the long-term vision for our data platform while mentoring engineers and influencing technical decisions across the organization.
Define and drive the technical roadmap for data infrastructure, establishing architectural patterns and standards that scale across the organization
Lead the design and implementation of complex, multi-system data architectures that support business-critical operations and enable innovation (data ingestion + export and delivery)
Evaluate and champion adoption of emerging technologies and best practices in data engineering, MLOps, and GenAI
Establish data governance frameworks, quality standards, and operational excellence practices across all data workloads
Drive cross-functional initiatives that require coordination between data, product, engineering, and business teams
Architect enterprise-scale data solutions for transferring data from first and third-party applications to and from our data warehouse
Design and oversee the development of robust, scalable APIs (REST, GraphQL, gRPC) that enable data access for internal teams and external partners
Lead the evolution of event-driven and API-first data architectures that support real-time data sharing and integration
Leverage Google Cloud (GCP) tools (Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM, etc.) and services (Astronomer - Apache Airflow) to architect and bring enterprise data workloads to production
Design resilient, self-healing data systems with comprehensive monitoring, alerting, and automated remediation - and participating as part of an on-call rotation.
Lead the evolution of our data platform on Google Cloud (GCP), leveraging advanced services and optimizing for cost, performance, and reliability
Define patterns for streaming and batch data architectures that serve diverse use cases
Establish best practices for data contracts, API versioning, CI/CD, documentation, and partner integrations
Lead MLOps strategy and implementation, establishing patterns for model deployment, monitoring, and governance at scale
Architect and oversee Generative AI infrastructure, enabling rapid prototyping while ensuring enterprise-grade security, compliance, and cost management
Partner with Data Science leadership to translate research initiatives into production-ready solutions
Drive innovation in AI/ML tooling and infrastructure, staying ahead of industry trends
Mentor and guide Data Engineers at all levels, conducting design reviews and providing technical feedback
Establish engineering standards, documentation practices, and knowledge-sharing processes
Participate in hiring and onboarding processes, helping to build a world-class data engineering team
Foster a culture of engineering excellence, experimentation, and continuous improvement
Partner with product, engineering, and business leaders to align data strategy with organizational goals
Making decisions as a team. The things you build will be maintained and improved upon by others; there is a shared responsibility to make defensible design considerations and high collaboration.
Communicate complex technical concepts to non-technical stakeholders, building alignment and driving informed decision-making
Represent data engineering in cross-functional planning and architecture forums
Build strong relationships with external partners and vendors
10+ years working in a data engineering role that supports incoming/outgoing feeds as well as analytics and data science teams
5+ years of advanced Airflow and Python experience writing production-grade, efficient, testable, and maintainable code
3+ years of experience designing, building, and maintaining production APIs (REST, GraphQL, gRPC) for data access and integration, including API gateway management, rate limiting, authentication/authorization, and versioning strategies
3+ years leading ML/MLOps initiatives, including model deployment, monitoring, and governance at scale
3+ years of hands-on experience with Google Cloud Platform (GCP) including Cloud Run, Cloud Functions, Vertex AI, Cloud Storage, IAM, and other core services.
Deep expertise with columnar databases (BigQuery, Snowflake, Redshift) and advanced SQL optimization techniques.
Demonstrated experience with AI Coding assistants – AI tools are heavily engrained in Bestow culture.
Proven track record designing an end-to-end data pipeline in cloud frameworks (such as GCP, AWS, Azure) with requirements from multiple stakeholders
Experience with upstream data coordination through data contracts.
Experience building CICD pipelines for data processing using tools such as Docker, CircleCI, dbt, git, etc
Extensive experience with infrastructure as code (Terraform, Pulumi) and GitOps practices
Expert level knowledge of data orchestration frameworks such as Apache Airflow (or similar) to manage SLOs and processing dependencies
Experience in building streaming / real-time ingestion pipelines
Experience with creating alerts and monitoring pipelines which contribute to overall data governance.
Experience with containerization and container orchestration technologies with cloud architecture and implementatio
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