Staff Data Platform Engineer

Healthcare should work for patients, but it doesn’t. In their time of need, they call down outdated insurance directories. Then wait on hold. Then wait weeks for the privilege of a visit. Then wait in a room solely designed for waiting. Then wait for a surprise bill. In any other consumer industry, the companies delivering such a poor customer experience would not survive. But in healthcare, patients lack market power. Which means they are expected to accept the unacceptable.

Zocdoc’s mission is to give power to the patient. To do that, we’ve built the leading healthcare marketplace that makes it easy to find and book in-person or virtual care in all 50 states, across +200 specialties and +12k insurance plans. By giving patients the ability to see and choose, we give them power. In doing so, we can make healthcare work like every other consumer sector, where businesses compete for customers, not the other way around. In time, this will drive quality up and prices down. 

We’re 18 years old and the leader in our space, but we are still just getting started. If you like solving important, complex problems alongside deeply thoughtful, driven, and collaborative teammates, read on.

We’re hiring a Staff Data Platform Engineer to define and lead the evolution of Zocdoc’s data platform at the intersection of data engineering and product engineering.

This role owns how data moves into, across, and out of our lakehouse and warehouse ecosystems. You’ll shape the contracts, access patterns, APIs, governance controls, and interoperability standards that enable teams to reliably produce and consume data at scale.

Unlike a traditional data engineering role, this position is focused on platform experience and data product design — building the frameworks, contracts, and service interfaces that make data trustworthy, compliant, and easy to use across both technical and non-technical stakeholders.

You will define how:

• Product teams publish high-quality, validated data into the platform
• Analytics and ML teams consume governed datasets with confidence
• Reverse ETL and activation patterns safely operationalize data back into product systems
• Access control, compliance, and governance are embedded into platform design by default

This is a highly cross-functional leadership role requiring deep data engineering expertise, strong product thinking, and the ability to influence standards across the organization.

• Passionate about designing data contracts and producer/consumer interfaces, not just pipelines.
• Excited to build APIs, SDKs, and shared packages that product engineers adopt.
• Motivated to define clear access patterns across warehouse, lakehouse, and downstream systems.
• Energized by solving governance, schema evolution, and compliance challenges at scale.
• Comfortable setting long-term architectural direction while still diving into implementation details.
• Experienced in building a data product used broadly across both technical and non-technical stakeholders.
• Excited about leveraging AI-assisted and agentic workflows to multiply engineering productivity.
• Thoughtful about the guardrails, access controls, and governance required when enabling GenAI systems to interact with sensitive data.

• Defining and evolving data contract standards across the company, including schema enforcement, versioning, and validation patterns.
• Designing interoperable ingestion and publishing frameworks that enable upstream producers (e.g., product engineering teams) to integrate seamlessly with the data platform.
• Building and standardizing APIs, libraries, or SDKs that simplify event logging, schema validation, and contract compliance.
• Establishing best practices for schema registry usage and distributed schema validation across streaming and batch systems (e.g., Kafka-based systems).
• Designing clear patterns for:
• When to use the data lake vs. the warehouse
• How curated layers are exposed
• How downstream consumers access data safely
• Leading reverse ETL and activation architecture to support operational use cases.
• Defining and enforcing access control, governance, and compliance standards (e.g., PHI/PII handling, DEID boundaries, RBAC).
• Partnering with Product Engineering, Security, Compliance, Analytics Engineering, and Infrastructure to align on standards and long-term direction.
• Mentoring engineers and influencing engineering culture around data quality, ownership, and contracts.
• Driving adoption of AI-assisted development practices (e.g., cloud-based coding environments, internal AI tooling, or agentic workflows) to accelerate platform delivery.
• Designing guardrails for AI access to data systems, including scoped permissions, auditing, and compliance-aware controls.
• Partnering with product and AI teams to ensure our data contracts, schemas, and curated layers are AI-consumable and safe by default.
• Evaluating how internal data platform assets can power AI use cases and intelligent automation across the company.

• 8+ years of experience in data engineering, platform engineering, or backend platform development.
• Demonstrated experience designing data contracts, schema governance, or producer/consumer standards at scale.
• Strong expertise in Python and SQL, with hands-on experience building scalable data frameworks.
• Experience with distributed data systems such as Spark (Databricks or EMR) and modern lakehouse architectures (Delta Lake / Iceberg).
• Experience with data warehouses such as Snowflake and strong understanding of performance and access patterns.
• Familiarity with schema registry systems and schema evolution in streaming systems (e.g., Kafka).
• Experience building APIs, shared libraries, or platform services adopted by multiple teams.
• Strong understanding of access control, RBAC, and compliance constraints in regulated environments.
• Proven ability to lead cross-functional architectural initiatives across product, analytics, and infrastructure teams.
• Clear communication skills and a track record of influencing standards across an organization.
• Experience working with AI-assisted development tools or cloud-based coding environments (e.g., Claude Code, Codex, Cursor, internal code generation frameworks, or similar systems).
• Strong understanding of governance considerations for GenAI systems, including access control, prompt safety, sensitive data handling, and auditability.
• Perspective on how structured data models and contracts improve AI reliability and downstream automation.

• Experience designing reverse ETL frameworks or operational activation pipelines.
• Familiarity with metadata and governance platforms (e.g., Unity Catalog, Collibra, OpenMetadata).
• Experience building internal developer platforms for event logging or data publishing.
• Experience working in regulated environments involving PHI/PII.
• Experience integrating streaming systems (Kafka/Kinesis) with warehouse and lakehouse ecosystems.