Senior Data Engineer

Role Overview

• Own the technical roadmap for our data platform, making architectural decisions that balance scalability, cost, and maintainability
• Evaluate and champion new technologies and tools, leading adoption across the organization
• Design systems for terabyte-scale data processing with a focus on performance and cost optimization
• Establish data engineering standards, patterns, and best practices that elevate team quality
• Model infrastructure costs and drive efficiency initiatives across Snowflake and cloud platforms
• Design and build scalable infrastructure for AI/ML workloads, including model training pipelines, feature engineering systems, and AI agent platforms
• Build reusable frameworks and internal platforms that abstract complexity for other engineers
• Design complex data models for critical business domains, balancing normalization, performance, and usability
• Architect real-time/streaming data infrastructure for low-latency use cases
• Engineer solutions for large-dataset performance challenges (Snowflake clustering, micro-partitioning, query optimization)
• Create sophisticated data-contract validators and schema evolution frameworks
• Develop profiling and drift detection systems that ensure data quality at scale
• Conduct design reviews and provide architectural guidance for major initiatives
• Lead incident response for critical data systems and drive blameless post-mortems
• Define SLAs and reliability standards for data products
• Mentor and upskill data engineers through pairing, code reviews, and technical discussions
• Lead cross-functional projects with analytics, data science, and product teams
• Build data observability platforms with intelligent alerting and monitoring
• Perform capacity planning and scalability analysis for the entire data stack
• Benchmark performance across infrastructure to identify optimization opportunities
• Design self-service tools that enable analytics engineers and analysts to work independently

Requirements

• Deep expertise with modern data warehouses (Snowflake strongly preferred) including advanced optimization techniques
• Strong foundations in distributed systems, data structures, and algorithms
• Production experience building high-volume data pipelines and ETL/ELT systems
• Proficiency in Python/SQL and orchestration tools (Airflow, dbt, or similar)
• Experience with cloud platforms (AWS/GCP) and infrastructure-as-code

Tech Stack

• Airflow
• AWS
• Cloud
• Distributed Systems
• ETL
• Google Cloud Platform
• Python
• SQL