Lead Data Platform Engineer

About This Role

We're seeking an exceptional Data Platform Engineer to shape the strategic direction and architecture of our data infrastructure. This is a high-visibility, high-impact role where you'll co-own the overall platform architecture, set technical standards across the entire Data Engineering organization, and drive initiatives that directly impact our data strategy. You'll be a force multiplier—inspiring teams, mentoring senior engineers, and making architectural decisions that affect how the entire organization builds and operates data systems. We are in the midst of transforming our legacy infrastructure into a modern cloud-first platform, and you'll be instrumental in leading this technical evolution.

What You'll Do

Strategic Platform Leadership

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 directly 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 and serve as an inspiration across all engineering teams

Architecture & Technical Excellence

Design and drive implementation of highly scalable platform architectures for multi-TB+ daily workloads

Author and sign off on all RFCs and technical specifications that touch your domain(s)

Define best practices and patterns that become standard across the organization

Build self-service infrastructure that empowers hundreds of data consumers while maintaining reliability

Design event-driven, real-time data architectures using Kafka and stream processing at scale

Contribute to any codebase across the organization, setting quality standards through exemplary code

Platform Engineering Excellence

Architect comprehensive Infrastructure as Code strategies and GitOps frameworks

Design enterprise-grade security frameworks including encryption, zero-trust access controls, and compliance

Establish SLAs/SLOs for critical platform services and drive observability strategy

Lead complex incident response, conduct thorough post-mortems, and implement systemic improvements

Drive platform reliability and performance optimization initiatives

Influence & Enablement

Mentor IC9 and IC10 engineers, accelerating their technical growth and impact

Collaborate across the entire Engineering organization as a thought partner

Interact with executives and external stakeholders as a domain expert

Democratize data access by empowering more teams to contribute to the data stack safely

Identify hiring gaps and help define interview panels for critical technical roles

Actively participate in and contribute to Data Engineering communities (conferences, Stack Overflow, vendor forums)

What You Bring

Deep Technical Expertise

Expert-level proficiency in SQL, Python, and at least one additional programming language

Demonstrated 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: 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

Platform Engineering Mastery

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

Experience & Background

10-15+ years of professional experience in data platform engineering or related field

Track record of leading complex, org-wide technical initiatives

History of driving architectural decisions that scale across large organizations

Experience in high-growth or large enterprise environments

Deep expertise with both modern and legacy technologies (Oracle, SQL Server, Informatica, SSIS, Business Objects)

Leadership & Influence

Proven ability to influence technical direction across multiple teams and domains

Excellence in mentoring senior engineers and accelerating their growth

Strong executive presence and ability to communicate complex technical concepts to all levels

Comfortable operating in high ambiguity and defining problems before solving them

Track record of building collaborative relationships across engineering and business organizations

Recognition as a thought leader in the Data Engineering community

Bonus Points

Deep experience with modern data stack tools (dbt, Looker, etc.)

Machine learning infrastructure and MLOps architecture expertise

Contributions to open-source data infrastructure projects

Published technical articles, conference talks, or thought leadership in data platforms

Expert knowledge of data privacy frameworks (GDPR, CCPA) and implementation at scale

What Success Looks Like

First 90 Days: Understand our architecture deeply, identify systemic opportunities and risks, and begin influencing key technical decisions. Establish yourself as a go-to expert in your domain(s).

First Year: Drive at least one major architectural initiative that impacts the entire Data Engineering organization. Establish yourself as a trusted advisor to leadership and a mentor to senior engineers. Author multiple RFCs that set new standards.

Ongoing: Continuously shape the technical direction of the organization, mentor the next generation of technical leaders, and maintain your position as one of the most knowledgeable experts in your domain(s) both internally and in the broader Data Engineering community.

Our Tech Stack

Cloud & Infrastructure: AWS (EC2, EKS, RDS, extensive cloud-native services)

Data Technologies: Apache Kafka, Apache Airflow, SQLMESH, Snowflake, dbt

Languages & Frameworks: Python, SQL, Java, Scala, Docker, Kubernetes

Monitoring & Observability: Prometheus, Grafana, DataDog, ELK Stack, Jaeger

Legacy Technologies: Apache Spark, Oracle, Postgres, SqlServer, Informatica, SSIS, Business Objects