Sr. Global Data Engineering Lead - Python

{"description": "SMBC Group is a top-tier global financial group. Headquartered in Tokyo and with a 400-year history, SMBC Group offers a diverse range of financial services, including banking, leasing, securities, credit cards, and consumer finance. The Group has more than 130 offices and 80,000 employees worldwide in nearly 40 countries. Sumitomo Mitsui Financial Group, Inc. (SMFG) is the holding company of SMBC Group, which is one of the three largest banking groups in Japan. SMFG's shares trade on the Tokyo, Nagoya, and New York (NYSE: SMFG) stock exchanges.

In the Americas, SMBC Group has a presence in the US, Canada, Mexico, Brazil, Chile, Colombia, and Peru. Backed by the capital strength of SMBC Group and the value of its relationships in Asia, the Group offers a range of commercial and investment banking services to its corporate, institutional, and municipal clients. It connects a diverse client base to local markets and the organization's extensive global network. The Group's operating companies in the Americas include Sumitomo Mitsui Banking Corp. (SMBC), SMBC Nikko Securities America, Inc., SMBC Capital Markets, Inc., SMBC MANUBANK, JRI America, Inc., SMBC Leasing and Finance, Inc., Banco Sumitomo Mitsui Brasileiro S.A., and Sumitomo Mitsui Finance and Leasing Co., Ltd.

Role Description

SMBC is in the process of leading Digital Transformation across our Americas Division as we continue to modernize our technology, focus on our data driven approach, grow and plan. As a result of this expansion, we are seeking an experienced and seasoned Data engineering lead for Data Quality & Governance as a part of the Data Management group. As the VP of Sr. Global Data Engineering Lead - Python within AD technology and Chief Data & Analytics office, you will lead the development and delivery of innovative data solutions that promote the firm's Data Governance and Regulatory success. In this strategic role, you will oversee large-scale projects, mentor technical teams, and collaborate with product, technology, and data partners to advance our Collibra cloud data platforms and engineering practices. You will also have responsibilities for administrative tasks such as attendance, timesheets, resource planning and performance reviews.

Role Objectives

• Own and execute the global data engineering strategy, aligning Python-based platforms and data pipelines with business, regulatory, and reporting priorities.
• Provide end-to-end accountability for enterprise-scale data engineering solutions, including backend services, APIs, and cloud-native data pipelines.
• Set and govern enterprise architecture standards for scalable, resilient, secure, and high-performance data platforms.
• Lead technical direction for Python engineering using Django, Flask, and FastAPI, driving adoption of RESTful, event-driven, and service-oriented architectures.
• Establish and enforce coding standards, design patterns, testing practices, and CI/CD pipelines to ensure quality, maintainability, and delivery speed.
• Oversee integration with data lakes, data warehouses, analytics platforms, and AI/ML ecosystems to support enterprise use cases.
• Partner with infrastructure, security, and cloud governance teams to ensure secure, compliant, and cost-efficient platform operations.
• Ensure strong engineering discipline across automated testing, code reviews, quality gates, performance tuning, and production stability.
• Drive innovation and modernization by evaluating and adopting new technologies, automation, and reusable platform capabilities.
• Own delivery of business-critical and regulatory data engineering initiatives, managing cross-region dependencies and multi-year roadmaps.
• Lead Agile and hybrid delivery execution, including JIRA epics, backlog grooming, prioritization, sprint planning, and release management.
• Monitor delivery health, velocity, risks, and dependencies, taking corrective action to ensure predictable and on-time outcomes.
• Own cloud-based platforms across Azure, or Google Cloud Platform, ensuring secure, compliant, and cost-efficient operations.
• Ensure production stability through strong engineering discipline, incident management, root-cause analysis, and proactive technical debt management.
• Lead vendor and strategic partner management, including selection, performance oversight, contract alignment, and third-party risk coordination.
• Build and retain high-performing global data engineering teams, mentoring senior leaders and strengthening long-term technical capability.

Required Qualifications:

• 8+ years of experience in Financial Services within enterprise or regulated environments.
• 10-15 years of experience in data governance, data management, data engineering, or enterprise technology roles, including 5+ years leading technology teams.
• Hands-on application development experience using Python (streamlit, django,), including automated unit testing and production support.
• Hands-on experience with Azure cloud, Kubernetes, OpenShift, and ArgoCD in production environments.
• Strong understanding of regulatory frameworks including FRB regulatory reporting, Volcker Rule, BCBS 239, and GDPR.
• Proven experience in banking or financial services, preferably supporting regulatory reporting, risk, credit, or finance domains.
• Hands-on expertise with Collibra Data Intelligence Platform and Collibra Data Quality, including architecture, installation, configuration, and enterprise rollout.
• End-to-end delivery experience covering system design, development, testing, deployment, and operational stability, with emphasis on Python-based platforms.
• Experience with DevOps and CI/CD tools, including GitHub or Bitbucket, Jenkins, and artifact repositories (PyPI or Maven).
• Bachelor's degree in computer science, MIS, Engineering, or related field, or equivalent experience.

Preferred Qualifications

• Relevant certifications such as Azure Solutions Architect Expert, Certified Kubernetes Administrator (CKA), Collibra Certified Expert, or Databricks.
• Experience with AI/ML frameworks and Agentic AI applications for data governance, data quality, or automation use cases.

SMBC's employees participate in a Hybrid workforce model that provides employees with an opportunity to work from home, as well as, from an SMBC office. SMBC requires that employees live within a reasonable commuting distance of their office location. Prospective candidates will learn more about their specific hybrid work schedule during their interview process. Hybrid work may not be permitted for certain roles, including, for example, certain FINRA-registered roles for which in-office attendance for the entire workweek is required.

SMBC provides reasonable accommodations during candidacy for applicants with disabilities consistent with applicable federal, state, and local law. If you need a reasonable accommodation during the application process, please let us know at ", "salary_raw": null}