• Evaluate and optimize the reporting layer of our Snowflake data warehouse to enhance cost efficiency and compute utilization. • Develop and implement strategies to improve query performance, reduce data processing time, and enhance overall efficiency. • Collaborate closely with the data engineering team to implement optimizations without compromising data integrity or security. • Utilize advanced analytic engineering techniques within Snowflake to optimize data transformations and computations. • Design efficient data models and schemas to support optimized reporting and analytics. • Implement best practices for data loading, storage, and retrieval to minimize costs and maximize performance. • Optimize query performance by tuning Snowflake configurations and query execution plans. • Implement caching strategies and materialized views to improve response times for commonly used reports. • Collaborate with data engineers to ensure efficient ETL processes and data transformations.

• Build ELT pipelines using BigQuery OR Snowflake, dbt, and cloud services • Write production-grade Python code for ingestion, transformation, and automation • Design analytics-ready data models • Work directly with customers to gather and clarify requirements • Ensure data quality via testing, validation, and monitoring • Debug pipeline failures, data issues, and performance bottlenecks • Participate in code reviews and follow engineering best practices • Work with 3–5 hours overlap with US EST/CST/PST

• Join a rapidly growing national firm at a formative stage • Make a visible, measurable impact on a rapidly growing business • Grow your skills, responsibility, and influence as the firm scales • Work alongside high-caliber, mission-driven teammates who care deeply about doing great work • Design, build, and maintain reliable data ingestion pipelines from internal systems and third-party data sources • Implement scalable ELT workflows that process and deliver data across the organization • Maintain transformation pipelines and ensure reliable delivery of analytics-ready datasets • Manage and optimize the performance, reliability, and scalability of the company’s cloud data warehouse environment • Maintain orchestration frameworks and scheduling systems that support data workflows • Optimize data pipeline performance, compute utilization, and system efficiency • Implement monitoring, alerting, and observability across data pipelines and platform components • Ensure data freshness and system uptime meet defined service expectations • Diagnose and resolve production issues including pipeline failures, data quality issues, and performance bottlenecks • Maintain version-controlled data infrastructure and CI/CD workflows for data pipelines • Implement testing and validation practices to ensure data quality and reliability • Partner with the Director of Data to implement data architecture and platform improvements • Support analytics and BI teams by ensuring reliable and well-modeled datasets are available for reporting and analysis • Contribute engineering input to platform improvements and technical roadmap initiatives

• Architecting and building the software and data infrastructure • Ensuring data is accurate, consistent, and trusted