Staff Data Engineer

Bestow is a leading vertical technology platform focused on modernizing the life insurance industry. As a Staff Data Engineer, you will serve as a technical leader, driving the vision for data infrastructure and mentoring engineers while collaborating with various teams to enhance data architecture and implement innovative solutions.

Responsibilities:

• 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

Requirements:

• 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 implementation features (single- and multi-tenancy, orchestration, elastic scalability)
• Deep understanding of standard IT security practices such as identity and access management (IAM), data protection, encryption, certificate, and key management
• Adaptability to learn new technologies and products as the job demands
• Proven ability to mentor engineers and lead technical initiatives across teams
• Familiarity with building tools that draw upon Generative AI (GenAI) integrations (Enterprise-grade, not simply vibe-coded)