SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
OpenFX is building infrastructure to power next-generation cross-border payment systems for institutions, processing billions in transaction volume monthly across global corridors. The company is backed by Accel, Lightspeed, and NfX, with a team drawn from JP Morgan, Goldman Sachs, FalconX, PayPal, Affirm, Polygon, Kraken, and Nium.
You will lead the Data Engineering pod, owning the complete data platform that ingests, transforms, and governs data flowing from trading, banking, settlement systems, and external bank and liquidity provider partners. Your scope spans ingestion (batch, streaming, CDC), the lakehouse architecture (bronze/silver/gold layers), orchestration, data quality, governance, cost optimization, and self-serve analytics enablement. You are accountable for the roadmap, engineering standards, and creating conditions for the team to deliver.
Key ownership areas include: reliable ingestion from production databases, event streams, files and APIs; lakehouse layer standards and data movement; orchestration, CI/CD and testing for data pipelines; data quality and freshness standards with incident response; governance for PII, access control, catalog, lineage, and compliance (GDPR, PCI DSS, DORA, SOC 2, ISO 27001); platform cost attribution and optimization; self-serve data discovery and use; ML/AI data foundations; and team growth.
You will stay hands-on: reviewing data models, debugging pipelines, and rolling up your sleeves when needed. You will grow the team by identifying hiring needs, closing roles, and investing in engineer development. The role emphasizes async communication and global distribution.
Success is measured by reliable, tiered datasets meeting freshness/completeness standards; canonical gold datasets as source of truth; automated, monitored ingestion from all sources; data incidents caught by monitoring and resolved operationally; governance controls holding up across compliance cycles; efficient cost scaling; self-serve analytics adoption; sustainable team workload and engineer growth; and strong hiring outcomes.
REQUIREMENTS:
Must-haves:
- 8+ years in data engineering, including 4+ years managing a data engineering team with senior engineers reporting
- Experience operating in regulated or audited environments
- Proven scale: production pipelines and lakehouse/warehouse serving multiple teams, with incident and migration experience
- Deep expertise in modern data engineering: batch and streaming ingestion, CDC, open table formats, layered data modelling, orchestration, data quality testing and observability
- Led a major platform decision (warehouse/lakehouse migration, orchestration change) from evaluation through production
- Can ship code: SQL and Python proficiency, ability to review data models and pipelines, debug failing jobs
- Demonstrated hiring and performance management
- Leadership style focused on growing people: listening, clearing obstacles, honest feedback
- Uses AI agents and assistants to automate data engineering toil
- Clear written communicator for async, globally distributed environments
What helps you stand out:
- Data engineering experience in fintech, payments, banking or exchanges
- Ingesting and normalizing money-movement data from multiple external banks and partners
- Rolling out data contracts, data catalogs, or data quality programmes across organizations
- Production streaming and CDC pipelines
- Hands-on experience with GDPR, PCI DSS, DORA, SOC 2 or ISO 27001 audits from the data perspective
