VP, Data Engineer
Job Location:
Charlotte, VT - USA
Monthly Salary:
Posted on:
Vacancies:
Job Summary
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 80000 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. SMFGs 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 organizations extensive global network. The Groups 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
Role Overview
We are seeking an experienced Vice President Data Engineer with 1015 years of handson experience to lead the design development and optimization of scalable cloudnative 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 handson levels delivering reliable secure and highperformance data solutions in a highly regulated investment banking environment.
Key Responsibilities
- Own the architecture design and implementation of endtoend ETL/ELT workflows using Azure Data Factory (ADF) and Azure Databricks for regulatory and compliancedriven 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 highperformance data pipelines supporting largescale sensitive financial datasets.
- Automate ingestion transformation and validation processes to enable near realtime 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 ACIDcompliant storage versioning and timetravel 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 SQLbased 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.
Qualifications and Skills
- Masters degree in Computer Science Engineering Information Systems or a related field.
- 1015 years of handson experience in data engineering preferably within financial services or other regulated industries
Required Technical Expertise
- 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 largescale datasets)
Preferred / NicetoHave Skills
- Experience designing and supporting enterprisescale 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 cloudnative 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 handson experience with AI agents intelligent automation or GenAIenabled data workflows is a strong plus.
- Excellent analytical communication and crossfunctional collaboration skills.
SMBCs 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
Required Experience:
Exec
