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TRM Labs

Senior Analytics Engineer

North AmericaFull-timeGlobal
šŸ“Š MidšŸ  Remote

Job Description

[AI-summarized by JobStash]

You will mature and scale the analytics data ecosystem. You will lead development and optimization of analytics pipelines and data models, define and implement analytics engineering best practices (testing, observability, versioning, documentation), improve scalability and maintainability, establish data quality solutions, investigate performance issues and implement durable improvements to reliability and cost, partner with data scientists, data engineers, product and business teams to deliver production-ready datasets, drive adoption of modern data tools and workflows, and participate in lightweight on-call responsibilities.

Requirements

  • ā—8+ years of experience in analytics engineering, data engineering, or data science with focus on building and scaling analytics workflows
  • ā—Strong experience across the entire data engineering lifecycle including ETLs, data model design, infrastructure, data quality, and architecture
  • ā—Deep proficiency in SQL and experience developing modular data models using dbt or equivalent
  • ā—Strong software engineering fundamentals including Python, CI/CD pipelines, and automated testing
  • ā—Proficiency in defining robust and scalable data models using best practices
  • ā—Experience using LLMs and enabling AI through high quality data infrastructure
  • ā—Hands-on experience with cloud data warehouses and infrastructure such as Snowflake, BigQuery, or Redshift and data orchestration tools like Airflow, Dagster, or Prefect
  • ā—Proficiency in developing dashboards using Looker, Tableau, Power BI, Plotly, or similar
  • ā—Excellent communication skills

Responsibilities

  • ā—Lead development and optimization of analytics pipelines and data models
  • ā—Define and implement analytics engineering best practices
  • ā—Improve scalability and maintainability of the analytics data ecosystem
  • ā—Deliver production-ready datasets in partnership with data scientists and product teams
  • ā—Establish and maintain data quality solutions
  • ā—Investigate performance issues and improve reliability, cost, and developer experience
  • ā—Drive adoption of modern data tools and workflows
  • ā—Participate in team on-call responsibilities

Tech Stack

SnowflakeAirflowBigQueryDagsterTableaudata warehouseLookerETLPower BIdata modeling
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