Key skills
SQLPythonAirflowData modelingDistributed processingData warehousingData lakehouseOrchestrationInterest in blockchainML infrastructureCollaborationCommunicationAdaptabilityOwnershipProblem-solvingMLMLflowData EngineeringData WarehousingSnowflakeBigQuerySparkdbtTerraformKubernetesDatadogBlockchainCI/CD
About this role
TRM Labs is a blockchain intelligence company committed to fighting crime and creating a safer world. As a Senior Data Engineer on the Attribution Expansion team, you will design, build, and operate the data infrastructure and services that turn noisy on‑chain activity into reliable attribution signals at scale.
Responsibilities:
- Build and evolve the Attribution Expansion infrastructure so other team members can safely ship new algorithms and data pipelines with minimal friction
- Own end‑to‑end attribution data pipelines that turn noisy on‑chain data into high‑quality labels and features used across TRM products
- Standardize how algorithms are implemented and modeled in data, making them reusable, testable, and explainable
- Implement robust monitoring, data quality checks, and SLAs for attribution systems
- Improve developer experience (templates, tooling, CI/CD, configuration) for other team members working on attribution pipelines
Requirements:
- Write high-quality code in SQL and Python
- Strong experience with Airflow, SQL and working with very large data sets in production
- Broad experience across data modeling, distributed processing, data warehousing / lakehouse, and orchestration
- Knowledge of data engineering stack (e.g., Spark/Flink/Beam, dbt, BigQuery/Snowflake/ClickHouse, Kubernetes/Terraform/Datadog, etc.)
- Demonstrated ownership and adaptability: able to take initiative, navigate ambiguity, structure complex problems, and ruthlessly prioritize the most critical issues
- Strong collaborator and communicator: works effectively across diverse, cross‑functional teams and clearly communicates complex ideas to both technical and non‑technical audiences, driven by TRM's mission to build a safer financial system
- Experience with ML infrastructure (feature stores, model registries, online inference data paths; tools like MLflow, Feast, ONNX, etc.)
- Interest in virtual currencies, applications, and their use in financial networks and blockchain analysis is a plus