Staff Data Engineer
Heavily uses AI coding assistants and builds GenAI integrations; culture embeds AI tools but expects enterprise-grade implementations rather than casual "vibe coding".
About the Role
Staff Data Engineer leading Bestow's data platform strategy and architecture, responsible for designing scalable data systems, APIs, and MLOps infrastructure on cloud platforms. The role mentors engineers, drives data governance and reliability, and partners across product and engineering to deliver production-grade data solutions.
Job Description
Role
Staff Data Engineer responsible for technical leadership and long-term vision of Bestow’s data platform. The role drives architecture and standards for data infrastructure, leads MLOps and GenAI infrastructure efforts, mentors engineers, and collaborates cross-functionally to deliver production-grade data pipelines, APIs, and integrations.
Key Responsibilities
- Define and drive the technical roadmap for data infrastructure, establishing architectural patterns and standards.
- Design and implement complex, multi-system data architectures for ingestion, processing, storage, export, and delivery.
- Architect and oversee APIs (REST, GraphQL, gRPC) and API gateway management for internal teams and external partners.
- Lead the evolution of event-driven and API-first architectures, streaming and batch patterns, and real-time ingestion pipelines.
- Leverage Google Cloud Platform services (Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM) and Astronomer/Apache Airflow to bring enterprise data workloads to production.
- Establish data governance frameworks, data contracts, quality standards, monitoring, alerting, and automated remediation; participate in on-call rotations.
- Lead MLOps strategy: model deployment, monitoring, governance, and GenAI infrastructure for rapid prototyping with enterprise-grade security and cost management.
- Evaluate and champion emerging technologies and best practices, and implement CI/CD and GitOps for data workflows.
- Mentor data engineers, conduct design reviews, define engineering standards, and participate in hiring and onboarding.
Requirements
- 10+ years in data engineering supporting incoming/outgoing feeds, analytics, and data science teams.
- 5+ years of production-grade Python and advanced Apache Airflow experience.
- 3+ years designing and maintaining production APIs (REST, GraphQL, gRPC), including gateway, rate limiting, auth, and versioning strategies.
- 3+ years leading ML/MLOps initiatives and 3+ years hands-on with Google Cloud Platform services (Cloud Run, Cloud Functions, Vertex AI, Cloud Storage, IAM).
- Deep expertise with columnar databases and SQL optimization (BigQuery, Snowflake, Redshift).
- Experience building end-to-end data pipelines in cloud frameworks (GCP, AWS, Azure) and upstream data coordination via data contracts.
- Experience with CI/CD for data (Docker, CircleCI, dbt, git) and infrastructure-as-code (Terraform, Pulumi) and GitOps practices.
- Expert-level knowledge of data orchestration frameworks (Apache Airflow) to manage SLOs and processing dependencies.
- Experience with streaming/real-time ingestion, containerization and container orchestration, monitoring/alerting, and standard IT security practices (IAM, encryption, key management).
- Demonstrated use of AI coding assistants and familiarity with building Generative AI integrations (enterprise-grade).
Compensation & Location
- Compensation Range: $187,500 - $220,500 (USD)
- Flexible remote/hybrid work; remote and work-from-home options available; office location in Dallas, TX for those who prefer onsite work.