Insider Risk Data Governance & Data Quality Lead

SMBC•Charlotte, NC

1d•Hybrid

About The Position

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 This critical role will be responsible for leading the Data Governance & Data Quality team for the Insider Risk program within AD Information Security department and will report to the Head of Insider Risk for SMBC AD. This is part of the overall cyber data initiative focusing on building the security and risk data platforms for the Cybersecurity Data Lakehouse (CyberDW). The goal of the CyberDW is to centralize the ISDAD data as well as establish effective data governance around the data sources and its data lineage. This newly formed role will collaborate with developers, data owners, governance leads and business analysts within the Information Technology (IT) department as well as other stakeholders aligned with the applications owned by ISDAD. Note: This is a hybrid role, requiring the successful candidate to attend our Charlotte office.

Requirements

Operate as the Data Quality lead for Insider Risk.
Analyze data sources, data dictionaries, business glossary, data mappings, ETL processes and data within CyberDW to understand their structure, relationships, dependencies to quantify and document the data quality rules, controls and lineage around the key data elements (KDEs).
Partner with the data owners and stakeholders to create KDEs and DQ rules needed for Insider Risk data.
Partner with the Data Owners and Data Stewards to understand what critical metrics and data fields are needed for Metric Dashboards.
Creation of technical requirements for implementation and maintenance of DQ rules in the Collibra Data Quality (CDQ) platform and KDEs in the Collibra Data Intelligence platform.
Partner with Technology and DQ Managers to ensure KDEs are governed by DQ Rules across various DQ dimensions of accuracy, completeness, uniqueness, timeliness, validity and consistency.
Support reporting on data quality metrics and trends.
Perform data validations and reconciliations to assess validity and effectiveness of the DQ rules, conduct QA and UAT, create and maintain detailed test plans, execute test cases, and test scripts to validate CDQ results.
Establish QA governance frameworks with clear accountability, traceability, and reporting across all test cycles.
Lead the QA Testing efforts that will design and execute test cases for CyberDW.
Identify, report, and track defects as well as participate in creating and maintaining regression test plans, cases, and scripts using Xray in Jira.
Design, develop, and test CyberDW Insider Risk ETL feeds focusing on data ingestion and data quality.
Create scripts and test plans to ensure that the raw data is transformed, processed, and stored correctly.
Work with the development teams and participate in daily scrum calls adhering to an agile SDLC framework.
Drive the adoption of test management tools to ensure complete coverage and defect traceability.
Leverage modern test harnesses, CI/CD pipelines, and AI-driven test frameworks for smarter, faster, and more reliable validation.
Utilize production job scheduling systems for the DQ and QC processes and adhere to standards around the daily scheduling and batch monitoring of production jobs.

Responsibilities

Operate as the Data Quality lead for Insider Risk.
Analyze data sources, data dictionaries, business glossary, data mappings, ETL processes and data within CyberDW to understand their structure, relationships, dependencies to quantify and document the data quality rules, controls and lineage around the key data elements (KDEs).
Partner with the data owners and stakeholders to create KDEs and DQ rules needed for Insider Risk data.
Partner with the Data Owners and Data Stewards to understand what critical metrics and data fields are needed for Metric Dashboards.
Creation of technical requirements for implementation and maintenance of DQ rules in the Collibra Data Quality (CDQ) platform and KDEs in the Collibra Data Intelligence platform.
Partner with Technology and DQ Managers to ensure KDEs are governed by DQ Rules across various DQ dimensions of accuracy, completeness, uniqueness, timeliness, validity and consistency.
Support reporting on data quality metrics and trends.
Perform data validations and reconciliations to assess validity and effectiveness of the DQ rules, conduct QA and UAT, create and maintain detailed test plans, execute test cases, and test scripts to validate CDQ results.
Establish QA governance frameworks with clear accountability, traceability, and reporting across all test cycles.
Lead the QA Testing efforts that will design and execute test cases for CyberDW.
Identify, report, and track defects as well as participate in creating and maintaining regression test plans, cases, and scripts using Xray in Jira.
Design, develop, and test CyberDW Insider Risk ETL feeds focusing on data ingestion and data quality.
Create scripts and test plans to ensure that the raw data is transformed, processed, and stored correctly.
Work with the development teams and participate in daily scrum calls adhering to an agile SDLC framework.
Drive the adoption of test management tools to ensure complete coverage and defect traceability.
Leverage modern test harnesses, CI/CD pipelines, and AI-driven test frameworks for smarter, faster, and more reliable validation.
Utilize production job scheduling systems for the DQ and QC processes and adhere to standards around the daily scheduling and batch monitoring of production jobs.