About Us
OpenFX is on a mission to move money as freely as data, unrestricted by time zones, banking hours, or legacy systems. We are building the infrastructure that will power the next generation of cross-border payment systems for institutions. The team's execution has been exceptional, and we're scaling at a remarkable pace. Our stellar early team comes with experience in companies like J.P. Morgan, Goldman Sachs, FalconX, PayPal, Affirm, Polygon, Kraken, Nium & others. We're backed by Accel, Lightspeed, NfX and other top-tier investors.
Role Overview
OpenFX processes billions of dollars in transaction volume every month across global corridors. Data Engineering owns the pipelines that carry that activity from our trading, banking and settlement systems, and from every bank and liquidity provider we work with, into the lakehouse: ingestion in batch and in real time, the bronze, silver and gold layers that turn raw events into canonical datasets, the orchestration that keeps them fresh, and the quality, governance and cost controls around them.
Key Responsibilities
- Lead and support the Data Engineering pod. You are accountable for the roadmap, the engineering standards and the data platform. You create the conditions in which the team can deliver them.
- Own ingestion. Bring every source into the lakehouse reliably, whether by batch, streaming or change data capture, from production databases and event streams to the files and APIs of our banking and liquidity partners.
- Own the lakehouse and its layers: raw data preserved in bronze, cleaned and conformed data in silver, and gold datasets that are modelled, documented and safe to build on. Set the standards for how data moves between them and who owns each layer.
- Own orchestration and the delivery path for data: scheduled and event-driven pipelines, CI/CD and testing for data code, environments, backfills and schema change.
- Own data quality and reliability. Define freshness, completeness and accuracy standards for tiered datasets, monitor them, and support incident response when data breaks.
- Own data governance in the platform: PII tagging and masking, access control, catalog and lineage coverage, retention and data residency, built to stand up to GDPR, PCI DSS, DORA, SOC 2 and ISO 27001 audits.
- Own the cost of the platform. Understand where storage and compute spend goes, attribute it to consumers, and keep it in line with the value it produces.
- Make the platform self-serve. Analysts, data scientists and product teams should find, understand and use gold datasets without opening a ticket.
- Own the data foundations for machine learning and AI: well-documented, governed datasets that models and agents consume safely, and the pipelines that carry model outputs back into the business.
- Stay close to the work. You know the platform well enough to review a data model, help debug a broken pipeline and roll up your sleeves when the team needs an extra pair of hands.
- Grow the team: know when the team needs another person and make the case, help close open roles, and invest in the engineers we already have so they keep growing in skill and scope.
What We Are Looking For
Must-haves:
- 8 or more years in data engineering, of which 4 or more years managing a data engineering team, with senior engineers reporting in.
- Has operated in a regulated or audited environment.
- Has seen scale. You have run production pipelines and a lakehouse or warehouse serving many teams, and learned from the incidents, migrations and growth that come with it.
- Deep in modern data engineering: batch and streaming ingestion, change data capture, open table formats, layered data modelling, orchestration, and data quality testing and observab