Significant professional experience in data engineering or backend/infrastructure engineering, with at least 3 years operating at a senior or staff level
Proven track record of owning architecture for data platforms or large-scale distributed systems
Deep expertise in AWS cloud services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora) and infrastructure as code (Terraform and/or CDK)
Expert-level SQL and Snowflake (or equivalent cloud data warehouse) knowledge, including performance tuning and cost optimization
Strong experience with dbt and modern ELT/ETL patterns at scale
Advanced Python skills with emphasis on building reusable libraries, frameworks, and tooling
Hands-on experience with orchestration frameworks (Airflow, Dagster, or Prefect) in production environments
Experience building data infrastructure for AI/ML: feature stores, training pipelines, embedding pipelines, model serving, or LLM integration
Deep understanding of streaming and event-driven architectures (Kinesis, Kafka, or equivalent)
Mastery of CI/CD, Git workflows, containerization (Docker), and deployment automation
Strong communication skills: ability to write technical RFCs, influence without authority, and translate complex trade-offs for non-technical stakeholders
Track record of mentoring and growing engineers, with a multiplier mindset