Staff Data Platform Engineer

Role Overview

• 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.

Requirements

• 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.

Tech Stack

• Cloud
• ETL
• Kafka
• Python
• Spark
• SQL

Benefits

• Flexible, hybrid work environment at our convenient Soho location
• Unlimited Vacation
• 100% paid employee health benefit options (including medical, dental, and vision)
• Commuter Benefits
• 401(k) with employer funded match
• Corporate wellness program with Wellhub
• Sabbatical leave (for employees with 5+ years of service)
• Competitive paid parental leave and fertility/family planning reimbursement
• Cell phone reimbursement
• Catered lunch everyday along with beverages and snacks
• Employee Resource Groups and ZocClubs to promote shared community and belonging
• Great Place to Work Certified