Senior Data Engineer

TopDog Law is a nationally scaling, fully-integrated personal injury law firm built for impact, excellence, and growth. The Senior Data Engineer will help build and scale the data platform that powers analytics, reporting, and data-driven decision-making across the organization.

Responsibilities:

• 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
• Contribute to documentation of data pipelines, system architecture, and platform dependencies
• 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

Requirements:

• 5+ years of experience building and maintaining production data pipelines
• Strong SQL skills and experience working with large datasets
• Experience with modern cloud data warehouses such as Snowflake or BigQuery
• Experience building transformation workflows using dbt or similar tools
• Experience working with orchestration tools such as Airflow
• Strong understanding of data pipeline reliability, performance optimization, and scalability
• Experience using version control systems such as Git in collaborative development environments
• Experience supporting data infrastructure used for machine learning workflows
• Experience building feature pipelines or supporting predictive modeling workloads
• Experience using Python for data processing and pipeline development
• Experience implementing monitoring and observability for data systems
• Experience working in high-growth or rapidly scaling environments
• Strong communication skills—written and verbal
• Ability to think critically, prioritize effectively, and execute with speed