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)