About the role

Senior Data Engineer building scalable features for blockchain applications and collaborating with cross-functional teams at TRM Labs.

Responsibilities

Build highly scalable features that integrate with dozens of blockchains.
Design and architect intricate data models for optimal storage and retrieval to support sub-second latency for querying blockchain data.
Collaborate across departments, partnering with data scientists, backend engineers, and product managers to design and implement novel data models that enhance TRM’s products.
Collaborate across departments, partnering with data scientists, backend engineers, and product managers to design and implement novel data models that enhance TRM’s products.
You’ll work closely with product managers, data scientists, and customer-facing teams to deeply understand user needs and translate them into scalable data solutions.”
Our best engineers are also exceptional communicators. They write clear design docs, proactively share tradeoffs, and build alignment across disciplines.
Bonus: Experience with LLMs or AI-powered workflows (e.g., prompt engineering, internal tooling, semantic search).

Bachelor's degree (or equivalent) in Computer Science or a related field.
A proven track record, with 5+ years of hands-on experience in architecting scalable API development, distributed system architecture, guiding projects from initial ideation through to successful production deployment.
Exceptional programming skills in Python, as well as adeptness in SQL or SparkSQL.
Versatility that spans the entire spectrum of data engineering in one or more of the following areas:
In-depth experience with data stores such as BigQuery and Postgres.
You’ve owned 0–1 systems: building pipelines, data platforms, or ML/BI workflows from scratch—not just maintaining legacy.”
You simplify complexity. You’re skilled at writing and communicating technical decisions clearly to both technical and non-technical stakeholders.
You’re cost-conscious: you design for performance, scale, and efficiency.
You’ve mentored engineers or analysts and enjoy leveling up those around you
In-depth experience with data stores such as BigQuery and Postgres.
Proficiency in data pipeline and workflow orchestration tools like Airflow and DBT.
Expertise in data processing technologies and streaming workflows including Dataflow, Spark, Kafka, and Flink.
Competence in deploying and monitoring infrastructure within public cloud platforms, utilizing tools such as Docker, Terraform, Kubernetes, and Datadog.
Proven ability in loading, querying, and transforming extensive datasets