VP, Data Engineer

SMBC•Charlotte, NC

3d•Hybrid

About The Position

SMBC Group is a top-tier global financial group with a 400-year history, headquartered in Tokyo. It offers a diverse range of financial services and has a significant worldwide presence. Sumitomo Mitsui Financial Group, Inc. (SMFG) is the holding company, and SMFG's shares are traded on major stock exchanges. In the Americas, SMBC Group operates across several countries, providing commercial and investment banking services to corporate, institutional, and municipal clients, leveraging its 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 Overview: We are seeking an experienced Vice President – Data Engineer with 10–15 years of hands‑on experience to lead the design, development, and optimization of scalable, cloud‑native data platforms. This role requires deep technical expertise in Azure, Databricks, PySpark, Python, and SQL, supporting enterprise data pipelines for regulatory, compliance, and analytics workloads. The ideal candidate will operate at both strategic and hands‑on levels, delivering reliable, secure, and high‑performance data solutions in a highly regulated investment banking environment.

Requirements

Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
10–15 years of hands‑on experience in data engineering, preferably within financial services or other regulated industries
Azure Databricks (clusters, jobs, notebooks, Delta Lake, performance tuning)
PySpark (RDDs, DataFrames, Spark SQL, optimization techniques)
Azure Cloud Services: Azure Data Factory (ADF), ADLS Gen2, Azure Synapse, Azure Functions
Azure DevOps / GitHub
Python for data engineering and automation workflows
SQL (complex queries, performance optimization, large‑scale datasets)

Nice To Haves

Experience designing and supporting enterprise‑scale ETL/ELT pipelines.
Strong understanding of Delta Lake, medallion architecture (Bronze/Silver/Gold), and distributed data processing.
Familiarity with data governance, security, encryption, and RBAC in cloud‑native environments.
Experience with CI/CD best practices and automated deployment pipelines.
Exposure to BI and visualization tools such as Power BI or Tableau.
Familiarity with Anaplan or other enterprise planning platforms is a plus, particularly in supporting downstream financial analytics or planning use cases.
Exposure to or hands‑on experience with AI agents, intelligent automation, or GenAI‑enabled data workflows is a strong plus.
Excellent analytical, communication, and cross‑functional collaboration skills.

Responsibilities

Own the architecture, design, and implementation of end‑to‑end ETL/ELT workflows using Azure Data Factory (ADF) and Azure Databricks for regulatory and compliance‑driven data ingestion and transformation.
Integrate, standardize, and normalize structured and unstructured data from multiple internal and external sources while enforcing strict data quality and governance controls.
Build secure, auditable, and high‑performance data pipelines supporting large‑scale, sensitive financial datasets.
Automate ingestion, transformation, and validation processes to enable near real‑time analytics and regulatory reporting.
Design and implement efficient storage formats, partitioning, and optimization strategies for fast data access and retrieval.
Utilize Databricks and Delta Lake for distributed processing, ACID‑compliant storage, versioning, and time‑travel capabilities.
Enforce data retention, archiving, and purging policies aligned with global regulatory and compliance requirements.
Drive the migration of legacy application logic into modern Azure Databricks, Data Lake, and SQL‑based architectures.
Maintain comprehensive data lineage, metadata management, and audit trails using Azure Purview or equivalent frameworks.
Partner with data governance, risk, and compliance teams to define data standards, access controls, and security requirements.
Implement and manage CI/CD pipelines using GitHub and GitHub Actions, enabling automated testing, version control, and reliable deployments.
Review code, enforce engineering best practices, and support production deployments and operational stability.
Participate in Agile/Scrum ceremonies, including sprint planning, design reviews, and regulatory or audit engagements.
Provide technical leadership and mentorship to data engineers, setting standards and best practices across the organization.