Geared toward data engineers with production experience in ClickHouse and Databricks, comfortable with designing and tuning high-throughput pipelines.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Merklescience via Lever
⚡️ About Merkle Science
Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies.
Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over US$27 MM from SIG, Beco, Republic, DCG, Kenetic, GGV and several others.
The Role
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 Do
Design 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.
What You Bring
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.
Nice to Have
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.
How We Work
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.
❤️ Well Being, Compensation and Benefits
We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance. We regularly host team-building sessions and encourage discussions around mental health.
We reward talent and believe in acknowledging people for their contributions. We offer industry-leading compensation, along with generous equity. As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.
Source: Merklescience careers (Lever)