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
