[Remote] Senior Analytics Engineer

Note: The job is a remote job and is open to candidates in USA. TRM Labs provides AI-powered intelligence solutions to help agencies investigate and disrupt crime. As a Senior Analytics Engineer on the Advanced Analytics team, you will lead the development and optimization of analytics pipelines and data models, ensuring high-quality data practices and improving the scalability of the analytics data ecosystem.

Responsibilities

• Lead the development and optimization of analytics pipelines and data models that power TRM’s products, investigations, and decision-making — enabling teams across the company and our customers (including former FBI, Secret Service, and Europol agents) to detect and respond to financial crime in the crypto space
• Define and implement best practices in analytics engineering (e.g., testing, observability, versioning, documentation), helping to level up the team’s development workflows and data reliability
• Improve the scalability and maintainability of our analytics data ecosystem, which process large volume of data, through thoughtful architectural decisions and tooling improvements
• Partner closely with data scientists, data engineers, product and business teams to deliver production-ready datasets that support models, metrics, and investigative workflows
• Establish best-in-class data quality solutions to increase the reliability and accuracy of our data
• Investigate performance issues, and bring creative, durable solutions to improve long-term reliability, cost and developer experience
• Drive adoption of modern data tools and workflows, helping the team evolve toward best-in-class analytics engineering practices
• Contribute to team on-call responsibilities that support the health and availability of our analytics and data science infrastructure (on-call is lightweight and shared equitably)

Skills

• 8+ years of experience in analytics engineering, data engineering, or data science with a strong focus on building and scaling analytics workflows
• Strong experience across the entire Data Engineering lifecycle (from ETLs, Data Model design, infra, Data Quality, architecture etc.)
• Deep proficiency in SQL and experience developing robust, modular data models using dbt (or equivalent tools) in a production environment
• Strong software engineering fundamentals, including experience with Python, CI/CD pipelines, and automated testing
• Proficiency in defining robust and scalable data models using best practices
• Experience using LLMs as well enabling AI through high quality data infrastructure
• Hands-on experience with cloud data warehouses and infrastructure (e.g., Snowflake, BigQuery, Redshift) and data orchestration tools (e.g., Airflow, Dagster, Prefect)
• Proficiency in developing compelling dashboards using tools like Looker, Tableau, Power BI, Plotly or similar
• Excellent communication skills — you can explain complex technical concepts to non-technical audiences and influence key decisions with clarity and data

Company Overview

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