Head of Data Platform Engineering

Pivotal Health; New York (NY); Hybrid; USD 240,000 - 270,000; Full time

Job summary

Pivotal Health is seeking a Head of Data Engineering to own the platform, architecture, and team behind our end-to-end data infrastructure. You will shepherd data pipelines, processing, storage, and APIs, enabling multiple teams to publish and consume clean data at scale.

You bring 10+ years in software/data infrastructure, strong code-first data architectures, and a bias for maintainable, secure, and observable systems.

Qualifications

• Strong Software Engineering background transitioning to data/infrastructure.
• 10+ years in software/data infrastructure with platform inheritance experience.
• Hands-on with modern data architectures (event streaming, distributed processing, lakehouses).
• Understanding that schema ownership belongs to the engineering team.
• Ability to review system designs and balance speed with technical debt.

Responsibilities

• Evolve and modernize data infrastructure with code-driven patterns.
• Build and lead a software-first data engineering team.
• Provide infrastructure and APIs for product teams to own domain models.
• Own the data lifecycle from ingestion to exposure for IDR and ML models.
• Promote software engineering best practices across pipelines, IaC, and observability.
• Implements security, access controls, and privacy guardrails.

Skills

Data infrastructure

Distributed systems

CI/CD

Cloud-native

Data platform

APIs

Software engineering

Code-first data architectures

Schema ownership

Security & compliance

Tools

Kafka

Spark

Lakehouse

Kubernetes

CI/CD tooling

Job description

Pivotal Health is seeking a Head of Data Engineering to own the platform, architecture, and team behind our end-to-end data infrastructure. You will shepherd data pipelines, processing, storage, and APIs, enabling multiple teams to publish and consume clean data at scale.

You bring 10+ years in software/data infrastructure, strong code-first data architectures, and a bias for maintainable, secure, and observable systems.