Executive Director - Data Engineering (Network) & Buenos Aires Site Lead

The Executive Director, Data Engineering is the functional leader for network data within Global Network Services, directly leading a 25 person engineering organization focuses on Network Data. Data is the foundation layer of the platforms we build: automation, observability, analytics, and AI all depend on trustworthy, queryable network state. This leader owns the network data platform end to end, from sourcing and modelling through to the serving layer that engineering teams consume, and sets the strategy for how network data is governed, delivered, and continuously improved at scale.

In parallel, the Executive Director serves as the Argentina (Buenos Aires) Regional Lead for Infrastructure Platform Foundational Services (Network, Storage, DataCentre, Data Protection and Recovery). Accountable for site strategy, talent development, governance, executive presence, and operational alignment across the region. The role establishes Argentina as a strategic engineering hub by building and sustaining high-performing engineering teams aligned to global priorities and outcomes.

This leader partners closely with the automation platform, SRE, and Product Engineering teams, and with firm-wide data governance and data quality programmes, to ensure network data is well governed and fit for production use.

Key Responsibilities

Functional Responsibilities - Data Engineering

• Define and execute the network data strategy, covering data modelling, authoritative sources, distribution, and how data is made available to consuming platforms
• Build and operate the data platform: ingestion and streaming pipelines, transformation, storage and modelling, and performant serving APIs and self-service access for engineering teams
• Establish consistent data models and identity standards for network entities (device, circuit, site, interface) so data can be reliably joined and reused across systems
• Drive data quality as an engineering discipline: validation, automated testing, monitoring, and measurable quality targets across the data lifecycle
• stablish data contracts and data SLOs covering freshness, completeness, accuracy, and availability, with clear ownership, versioning, and schema-evolution standards
• Implement lineage and observability across data pipelines so issues can be traced to source and change impact can be assessed ahead of time
• Partner with firm-wide data governance and data quality programmes to meet audit, risk, and regulatory obligations
• Make network data AI-ready: structured, well-described, and accessible so agentic and LLM-driven capabilities can reason over accurate state, and apply AI within the data lifecycle itself
• Establish engineering standards, testing frameworks, and CI/CD pipelines so data pipelines and services are production-grade, secure-by-design, and observable

• Deliver measurable outcomes: data SLO attainment, improved data quality metrics, faster access to network data, and demonstrable downstream gains in automation, reliability, and analytics

  Regional Responsibilities - Argentina IP Foundational Services Site Lead

• Represent the Argentina hub in global Infrastructure Platforms Foundational Services forums.
• Drive site culture, retention, and consistent engineering standards across functional silos
• Ensure effective cross-functional collaboration with security, infrastructure, application teams, service management, and business partners to deliver integrated outcomes
• Manage regional resource planning and budget inputs: capacity forecasting, skills coverage, on-call sustainability, and investment recommendations tied to measurable service improvements
• Hire, develop, and retain engineering talent across the Argentina footprint

• Ensure governance, risk, and audit compliance for in-country operations

  Leadership Expectations

• Strong ownership of data platform and data quality outcomes
• Ability to build and scale engineering teams as a manager-of-managers across functional and matrixed reporting lines
• Drives a product-led approach to data, with clear ownership, published standards, and consumers treated as customers
• Strong executive communication skills, including the ability to make the business case for foundational data investment
• Maintains compliance and control discipline

• Credible senior technology presence in-region, able to represent JPMC externally with regulators, universities, and partners

  Required Qualifications, Capabilities, and Skills

• 10+ years in data engineering or data platform engineering, including leadership roles running data systems at scale and manager-of-managers experience
• Proven ownership of a production data platform: pipelines, modelling, storage, and serving, with accountability for freshness, correctness, and availability
• Demonstrated experience establishing data governance in practice: ownership models, data contracts, data quality measurement, lineage, and schema evolution
• Strong hands-on engineering foundation (Python, SQL, APIs, streaming and batch pipelines, modern data stores) and the judgment to hold a high engineering bar
• Proven track record adopting agentic AI and LLM-driven capability in production engineering environments, and preparing data estates for AI consumption
• Experience operating within formal risk and control frameworks: audit engagement, evidence quality standards, and remediation governance
• Proven ability to scale teams and drive adoption of common data standards across partner teams
• Strong global collaboration skills across distributed, matrixed organizations
• Prior site, country, or regional engineering hub leadership experience

• BS/BA degree or equivalent practical experience in technology, engineering, or a related discipline

  Preferred Qualifications, Capabilities, and Skills

• Infrastructure or network domain experience: inventory, topology, configuration, and telemetry data. A strong plus, not a gate
• Experience with source-of-truth platforms, or graph and topology data models
• Experience with streaming telemetry and time-series data at scale
• Financial services or other regulated environment experience
• Experience supporting automation and reliability engineering teams as primary data consumers
• Demonstrated use of AI to redesign data engineering workflows for measurable impact, and to build organizational AI fluency
• Demonstrated experience in incident, problem, and change governance, including executive communications during high-severity events

Team Scope

• Direct Org: 25 engineers (network data engineering), globally distributed
• Growth aligned to the Network Services data platform build-out
• Partnership: strong partnership with the automation platform and SRE teams, global Product Engineering, firm-wide data governance and data quality programmes, and regional Infrastructure leadership
• Regional (matrix): site leadership for all IP Foundational Services engineers in Argentina