Engineering Manager - Data

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.

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 changes.
• 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.

Requirements:

• 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 observability.
• Has led a major platform decision, such as a warehouse or lakehouse migration or an orchestration change, from evaluation through to production.
• Can still ship code when needed. You can write SQL and Python, review a data model and a pipeline, and debug a failing job.
• Demonstrated hiring and performance management.
• Leads by growing the people around you. Your former reports would say you listened first, cleared the way for them, and were honest with them.
• Uses AI agents and assistants to automate the toil out of data engineering work.
• Clear written communicator. We are async-heavy and globally distributed.

What Helps You Stand Out:

• Experience running data engineering in fintech, payments, banking, or exchanges.
• Ingesting and normalizing money-movement data from many external banks and partners.
• Rolling out data contracts, a data catalog, or a data quality program across an organization.
• Streaming and change-data-capture pipelines in production.
• Hands-on with GDPR, PCI DSS, DORA, SOC 2, or ISO 27001 audits from the data side.

What Success Looks Like:

• Reliable pipelines: Tiered datasets have defined freshness and completeness standards and continue to meet them.
• Canonical data: Gold datasets are modelled once, documented, owned, and used as the source of truth across finance, compliance, and the business.
• Source coverage: Every production system, bank, and liquidity provider feeds the lakehouse through automated, monitored ingestion.
• Operational health: Data incidents are caught by monitoring, resolved with the on-call team, and post-mortem actions are followed through.
• Governance: PII is tagged and masked, catalog and lineage coverage is high, and controls hold up across GDPR, PCI DSS, DORA, SOC 2, and ISO 27001 cycles.
• Cost: Storage and compute spend scale efficiently with data volume, with clear attribution by consumer.
• Self-serve: Teams find and use the data they need without depending on the data engineering queue.
• Team: Engineers are growing in skill and scope, feel trusted to lead their own work, and the workload is sustainable.
• Talent: Strong engineers want to join and stay, and you raise the bar of every hiring loop you sit on.