Job description

About the company Hi, we're Ondo Finance. Our mission is to provide institutional-grade, blockchain-enabled investment products and services. We have both a technology arm that develops decentralized finance technology, and an asset management arm that creates and manages tokenized funds. We are the global leader in tokenized treasuries, tokenized stocks and ETFs, and are building the future of institutional-grade financial services onchain. Founded by alumni of Goldman Sachs' Digital Assets team, we're backed by some of the best investors in the world, including Founders Fund, Coinbase Ventures, Pantera Capital, Tiger Global, and more. We are the category leader by AUM, well-capitalized, and growing quickly. We're fully remote, with team members across the U.S. About the role Our Data Science team turns on-chain and off-chain data into the analytics, risk, and decision-making infrastructure behind Ondo Global Markets (tokenized stocks) and Ondo Perps. We're hiring Ondo's first Data Engineer to build the foundational data platform that this work depends on—consolidating fragmented data sources, standing up a unified warehouse in BigQuery, and making trustworthy, well-modeled data available to the entire team. This is a hands-on infrastructure role with end-to-end ownership. You'll lead the migration of analytics into BigQuery, work directly in the Go services that power Ondo Perps to ingest additional data into the warehouse, and build the repeatable ETL pipelines that let data scientists, risk, and analytics move quickly and self-serve. You'll partner closely with Data Science, Backend Engineering, Risk, and Product to replace today's siloed, ad-hoc pipelines with reliable, observable, and reusable data infrastructure. Our team operates with high autonomy, high trust, urgency, and an uncompromising quality bar. We're looking for someone who improves the platform without waiting to be asked, cares deeply about data quality and correctness, and pairs strong engineering judgment with humility. You'll shape both Ondo's data foundation and how our Data Science team builds and scales analytics. Responsibilities Lead the migration of analytics and existing data workflows into BigQuery, establishing it as Ondo's unified, trusted warehouse and reporting layer. Read, understand, and extend the Go services powering Ondo Perps to ingest additional data into the warehouse (e.g. changefeeds and streaming/batch sinks from production systems). Design, build, and operate repeatable, well-tested ETL/ELT pipelines that the wider Data Science team can rely on and reuse. Consolidate fragmented and siloed data sources—internal databases (CockroachDB, QuestDB), on-chain and market-data vendors (Allium, Dune, Databento), and product analytics—into consistent, well-modeled datasets. Establish standards for data ingestion, transformation (e.g. dbt), orchestration, testing, lineage, and documentation so definitions are consistent and self-serve. Experience building foundational metrics and dashboards, with strong opinions on data lineage and quality, in partnership with Data Science. Own data quality, observability, monitoring, access controls, and cost management across the pipelines and warehouse. Partner with Data Science, Backend Engineering, Risk, and Product to turn analytical and reporting needs into durable data infrastructure. Requirements 5+ years of data engineering experience building and operating production data pipelines and warehouses. Deep hands-on experience with a cloud data warehouse—BigQuery strongly preferred—including performance, partitioning, and cost optimization. Strong SQL and data modeling skills, plus experience with modern transformation and orchestration tooling (e.g. dbt, Airflow, Dagflow-style schedulers, or similar). Experience designing batch and streaming ingestion (CDC/changefeeds, event pipelines) from operational databases into a warehouse. Experience building data infrastructure for fintech, trading, or other high-integrity financial systems where correctness and auditability matter. High standards for data quality, reliability, testing, observability, and documentation. Clear communication, high ownership, and a track record of delivering effectively with both technical and non-technical partners. Nice to haves Proficiency reading and writing Go, or strong general software-engineering ability and a track record of quickly ramping on unfamiliar production codebases. Experience with crypto-native or on-chain data, and vendors such as Allium, Dune, or Databento. Familiarity with CockroachDB, QuestDB, Snowflake, or similar operational and analytical stores. Experience enabling self-serve analytics and AI-over-data workflows for analytics and data science teams. Exposure to risk, quantitative analytics, or trading microstructure data. What we offer Competitive compensation including salary, future token rights, and/or equity, according to your preferences. We're well-funded and believe that great talent deserves great compensation. Full benefits, including medical, vision, and dental coverage, plus a flexible vacation policy. A remote-first team across many countries. You'll be an early team member helping shape our vision, culture, and engineering practices. A+ colleagues. Our team includes alumni from Goldman Sachs, BlackRock, Two Sigma, Bridgewater, SpaceX, AWS, Meta, Google, McKinsey, Coinbase, Circle, and Uniswap. Best-in-class investors. We are proud to be backed by leading crypto experts and venture firms, including Pantera Capital, Founders Fund, and Coinbase Ventures.

Requirements

Must-haves

Lead the migration of analytics and existing data workflows into BigQuery, establishing it as Ondo's unified, trusted warehouse and reporting layer.
Read, understand, and extend the Go services powering Ondo Perps to ingest additional data into the warehouse (e.g. changefeeds and streaming/batch sinks from production systems).
Design, build, and operate repeatable, well-tested ETL/ELT pipelines that the wider Data Science team can rely on and reuse.
Consolidate fragmented and siloed data sources—internal databases (CockroachDB, QuestDB), on-chain and market-data vendors (Allium, Dune, Databento), and product analytics—into consistent, well-modeled datasets.
Establish standards for data ingestion, transformation (e.g. dbt), orchestration, testing, lineage, and documentation so definitions are consistent and self-serve.
Experience building foundational metrics and dashboards, with strong opinions on data lineage and quality, in partnership with Data Science.
Own data quality, observability, monitoring, access controls, and cost management across the pipelines and warehouse.
Partner with Data Science, Backend Engineering, Risk, and Product to turn analytical and reporting needs into durable data infrastructure.
5+ years of data engineering experience building and operating production data pipelines and warehouses.
Strong SQL and data modeling skills, plus experience with modern transformation and orchestration tooling (e.g. dbt, Airflow, Dagflow-style schedulers, or similar).
Experience designing batch and streaming ingestion (CDC/changefeeds, event pipelines) from operational databases into a warehouse.
Experience building data infrastructure for fintech, trading, or other high-integrity financial systems where correctness and auditability matter.
High standards for data quality, reliability, testing, observability, and documentation.
Clear communication, high ownership, and a track record of delivering effectively with both technical and non-technical partners.

Nice-to-haves

Deep hands-on experience with a cloud data warehouse—BigQuery strongly preferred—including performance, partitioning, and cost optimization.
Proficiency reading and writing Go, or strong general software-engineering ability and a track record of quickly ramping on unfamiliar production codebases.
Experience with crypto-native or on-chain data, and vendors such as Allium, Dune, or Databento.
Familiarity with CockroachDB, QuestDB, Snowflake, or similar operational and analytical stores.
Experience enabling self-serve analytics and AI-over-data workflows for analytics and data science teams.
Exposure to risk, quantitative analytics, or trading microstructure data.

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