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Senior Analytics Engineer

Posted about 1 month agoViewed

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💎 Seniority level: Senior, 8+ years

📍 Location: United States, PST

💸 Salary: 160000.0 - 175000.0 USD per year

🔍 Industry: Blockchain Intelligence

🏢 Company: TRM Labs👥 101-250💰 $70,000,000 Series B over 2 years agoCryptocurrencyComplianceBlockchainBig Data

🗣️ Languages: English

⏳ Experience: 8+ years

🪄 Skills: PythonSQLCloud ComputingSnowflakeTableauAirflowData engineeringData scienceCommunication SkillsCI/CDData modeling

Requirements:
  • 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.
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).
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