You will design, build and run the systems behind our data products, covering ingestion, processing, analytical storage and serving. You own your work from the design document through to production. The people who design a pipeline also run it and answer for its numbers.
Our stack is very wide, and we go deep in every part of it. We don't stop at "it works." We keep measuring, profiling and tuning to make systems faster, cheaper and more reliable. We want engineers who are razor sharp, pay close attention to detail, and care as much about why a query takes 800 ms as whether it returns the right answer.
You don't need blockchain experience. If you're a strong data engineer who wants to learn it, we'd like to hear from you.
What You'll DoDesign and build scalable, high-throughput data pipelines and platform systems for blockchain data across 30+ chains.
Build backend data services and loaders in Go and Python.
Design and evolve data models and analytical workloads in ClickHouse, and tune them for sub-second queries.
Build incremental, idempotent pipelines on Databricks over billions of rows and tens of terabytes.
Keep improving the scale, reliability, freshness, performance and cost efficiency of the platform.
Investigate hard performance and reliability problems and see them through to resolution.
Work closely with the product, analytics and investigations teams that use the data, and turn their needs into platform designs.
Help set technical direction through architecture discussions, design reviews and code reviews.
Mentor engineers and help set strong engineering and operational practices.
5+ years building and operating production data systems, including ETL and ELT pipelines. At least two of those years should be spent owning systems that other engineers depend on.
Expert Python and strong SQL. You write typed, tested code, are comfortable with asyncio and concurrency, and have built incremental data models over billions of rows.
Go for backend services. You have built and run high-throughput services or data loaders in Go in production.
ClickHouse in production. You know MergeTree schema and sort-key design, materialised views, bulk loads and query tuning.
Databricks in production. You have worked with PySpark, Delta Lake and Unity Catalog, designed incremental and idempotent tables, and kept an eye on compute cost.
Large-scale data. You have hands-on experience with pipelines over billions of rows and tens of terabytes. You understand how partitioning, clustering and file sizing affect cost and latency.
Kafka, Airflow and Kubernetes in production. You understand delivery semantics and idempotent writes, have handled backfills and failures, and have run data workloads with Docker and Helm. Google Cloud is preferred; AWS is fine.
An operations and communication mindset. You instrument what you build, write runbooks and post-incident reviews, and can write a design document that a reviewer in another time zone can approve.
Blockchain data: EVM, UTXO or non-EVM chain data models, and pulling data from nodes over JSON-RPC or gRPC.
Graph or distributed key-value stores: entity resolution or clustering on large graphs.
Infrastructure as code, GitOps and observability: Terraform, Helm, ArgoCD, OpenTelemetry, Prometheus and Grafana, and load-test design.
Experience building platforms that serve multiple data sources, datasets or product teams.
AI-assisted development, and experience in fintech, compliance or AML.
You'll join a small, senior team where everyone owns their work end to end.
We make decisions based on measured evidence, not opinion.
We keep design notes and runbooks next to the code.
We test at production scale and run blameless post-incident reviews.
We use AI coding tools every day, and we use judgement when reviewing what they produce.
Skills Required
- 5+ years building and operating production data systems, including ETL and ELT pipelines
- At least two years owning systems that other engineers depend on
- Expert Python and strong SQL skills, including typed and tested code, asyncio, concurrency, and incremental data models over billions of rows
- Production experience building and operating high-throughput services or data loaders in Go
- Production experience with ClickHouse, including MergeTree schemas, sort-key design, materialized views, bulk loads, and query tuning
- Production experience with Databricks, PySpark, Delta Lake, Unity Catalog, incremental and idempotent tables, and compute cost management
- Hands-on experience with pipelines processing billions of rows and tens of terabytes
- Understanding of partitioning, clustering, and file sizing impacts on cost and latency
- Production experience with Kafka, Airflow, and Kubernetes
- Experience with delivery semantics, idempotent writes, backfills, failures, Docker, and Helm for data workloads
- Operations and communication mindset, including instrumentation, runbooks, post-incident reviews, and design documentation
- Blockchain data experience with EVM, UTXO, or non-EVM chain data models and JSON-RPC or gRPC
- Experience with graph or distributed key-value stores for entity resolution or clustering
- Experience with infrastructure as code, GitOps, observability, Terraform, Helm, ArgoCD, OpenTelemetry, Prometheus, or Grafana
- Experience designing load tests
- Experience building platforms serving multiple data sources, datasets, or product teams
- AI-assisted development experience and fintech, compliance, or AML experience
- Google Cloud experience
What We Do
Founded in 2018, Merkle Science is the next generation predictive cryptocurrency risk and intelligence platform that helps crypto companies, financial institutions, and government entities detect, investigate, and prevent illegal activities involving cryptocurrencies. Merkle Science’s proprietary Behavioral Rule Engine enables our tools to go beyond blacklists so that compliance teams may fulfill their local KYC/AML obligations and industry players may stay keep pace with the industry’s increasingly complex illicit activities. Merkle Science envisions a world powered by crypto and is creating the infrastructure necessary to ensure the safe and healthy growth of the cryptocurrency industry as it becomes a key pillar of the $22 trillion financial services ecosystem. We enable businesses to scale and mature so that a full range of individuals, entities, and services may transact with crypto safely.
