Lead Data Platform Engineer

You'll shape the strategic direction and architecture of the company's data infrastructure in this high-visibility, high-impact role. You'll co-own the overall platform architecture, set technical standards across the entire Data Engineering organization, and drive initiatives that directly impact the data strategy. You'll inspire teams, mentor senior engineers, and make architectural decisions that affect how the entire organization builds and operates data systems, while leading the transformation of legacy infrastructure into a modern cloud-first platform.

Responsibilities

• Co-own and evolve the overall data platform architecture across all domains
• Guide the technical vision for the entire Data Engineering organization
• Drive platform initiatives that support the company's data strategy
• Lead the modernization of legacy systems while ensuring business continuity
• Identify and solve problems that span the entire Data Engineering org before they impact delivery
• Set the bar for excellence across engineering teams
• Design and drive implementation of scalable platform architectures for multi-TB+ daily workloads
• Author and sign off on RFCs and technical specifications
• Define best practices and patterns for the organization
• Build self-service infrastructure for data consumers
• Design event-driven real-time data architectures using Kafka and stream processing
• Contribute to codebases across the organization
• Architect Infrastructure as Code strategies and GitOps frameworks
• Design enterprise-grade security frameworks including encryption and zero-trust access controls
• Establish SLAs/SLOs for critical platform services and drive observability strategy
• Lead complex incident response and conduct post-mortems
• Drive platform reliability and performance optimization initiatives
• Mentor IC9 and IC10 engineers
• Collaborate across the Engineering organization
• Interact with executives and external stakeholders
• Democratize data access across teams
• Identify hiring gaps and define interview panels
• Participate in Data Engineering communities

Requirements

• Expert-level proficiency in SQL, Python, and at least one additional programming language
• Mastery of object-oriented design and software engineering principles
• Deep expertise with databases across paradigms: PostgreSQL, MySQL, Snowflake, Oracle, DynamoDB, MongoDB
• Extensive Kafka ecosystem expertise including cluster management, Debezium, schema registry, Connect frameworks, stream processing
• Advanced container orchestration with Kubernetes and Docker at scale
• Expert knowledge of Infrastructure as Code, GitOps, and cloud-native architectures
• Proven track record architecting platforms processing multi-TB daily workloads
• Experience designing and operating systems supporting hundreds of concurrent users
• Deep understanding of distributed systems, cloud architectures, and scalability patterns
• Expert-level experience with observability stacks (Prometheus, Grafana, ELK, Jaeger)
• Mastery of data orchestration platforms like Airflow, Prefect, or Dagster
• Demonstrated success modernizing legacy data systems while ensuring zero business disruption
• 10-15+ years of professional experience in data platform engineering or related field
• Track record of leading complex, org-wide technical initiatives
• Experience in high-growth or large enterprise environments
• Deep expertise with modern and legacy technologies (Oracle, SQL Server, Informatica, SSIS, Business Objects)
• Proven ability to influence technical direction across multiple teams and domains
• Excellence in mentoring senior engineers