Staff Software Engineer (Data Platform)

Location: Hybrid in Mountain View, Los Angeles, New York City, or San Francisco, United States

Salary: $210,000–$275,000 annual base salary for Zone A, plus equity

Company

hirify.global is building an AI-native healthcare operating system spanning clinical documentation, patient and revenue workflows, claims processing, and integrations with more than 60 EHRs.

What you will do

• Own the data warehouse platform end to end, including CDC pipelines, data lake, query layer, transformation layer, and analytics tooling.
• Design and operate low-latency CDC and streaming pipelines using Debezium and Kafka, Redpanda, or an equivalent backbone.
• Architect an object-storage data lake using Iceberg, Delta Lake, or Hudi with Parquet for batch and streaming workloads.
• Run and scale StarRocks or similar MPP and lakehouse engines, including schema design, materialized views, ingestion, tuning, and cost-performance trade-offs.
• Build dbt modeling, testing, documentation, semantic-layer, orchestration, CI/CD, observability, and data-quality capabilities.
• Partner with Security and Compliance on PHI/PII handling, access controls, lineage, auditability, and self-serve platform conventions.

Requirements

• 6+ years of software engineering experience, including significant experience building or operating data platforms at scale.
• Production experience across CDC, streaming, data lake formats, MPP or lakehouse query engines, and dbt.
• Strong SQL, schema design, query optimization, and large-dataset cost and latency analysis skills.
• Experience operating production data infrastructure, including orchestration, observability, on-call, data quality, and incident response.
• Experience with Debezium, StarRocks, and dbt in production.
• Ability to work in a hybrid arrangement from one of the listed United States locations.

Nice to have

• Experience with semantic layers, data catalogs, or lineage tools such as Cube, DataHub, OpenMetadata, or Amundsen.
• Experience with HIPAA-regulated data, PHI handling, de-identification, and access governance.
• Experience supporting AI/ML workloads, including feature stores, training-set curation, embedding pipelines, or retrieval systems.
• Experience across AWS, GCP, and Azure, as well as Terraform, Pulumi, or Kubernetes controllers.

Culture & Benefits

• Work directly with clinicians and see the impact of platform decisions on patient care.
• Move quickly with daily deployments and ownership from early design through production.
• Operate in a high-stakes environment focused on difficult technical problems and high engineering standards.
• Receive equity in addition to the base salary.