Senior Data Engineer - Vice President

Sumitomo Mitsui Banking Corporation•Charlotte, NC

2d•Hybrid

About The Position

The VP, Senior Data Engineer is a critical role within the Information Security department and will report to the Director, Information Security Data Operations for the SMBC Americas Division. This VP role will be responsible for designing, developing & deploying cloud data solutions for ISDAD. This is part of the overall cyber data initiative focusing on building out the security and risk data platform for Information Security. This individual will be responsible for developing the data feeds and will be a part of the larger development effort of building out a 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 role will collaborate with the 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.

Requirements

Bachelor’s degree in Computer Science, Information Security, Data Management, or related field
10+ years’ experience in IT development, data governance, data architecture or related roles, preferably in a highly regulated environment such as financial services
Strong knowledge of CI/CD and DevOps tooling (i.e., Git, Jenkins, Azure DevOps)
Strong knowledge of Azure Databricks, Azure Data Factory, Azure Functions, Azure Data Lake storage, Azure Event Grid, Azure Log Analytics, Azure Monitor, Unity catalog repository configuration
Proficient in data management & data modeling tools (e.g., Collibra DQIM/DQE, IBM Infosphere DA)
Proficient in SQL Server, Oracle / PL-SQL, T-SQL and SQL stored procedures
Proficient in Python, Java or similar high-level server-side languages
Strong knowledge of enterprise Information Security data (i.e., Phishing, Identity Management, Privileged Access, Cloud Security, Incident Response, Vulnerability Management, Threat Detection)
Data knowledge of PaaS/SaaS products (i.e., ServiceNow, Crowdstrike, MS Purview, Proofpoint, WIZ.IO, JIRA, SharePoint, Azure Active Directory, SAI360)
Understanding of information security frameworks (i.e., NIST, CIS, CRI Profile) and regulatory compliance (i.e., NYSDFS, GDPR, CCPA)
Experience with REST API web services and microservice architecture
Strong understanding of ETL/ELT
Problem solving and analytical skills, with an initiative-taking and results oriented approach

Nice To Haves

Knowledge of Microsoft Sentinel for security information and event management (SIEM) is a plus
Knowledge of IBM Tivoli Workload Scheduler a plus
Exposure to PowerBI for data visualization and reporting is a plus

Responsibilities

Design, develop, testing & support of cloud data solutions for the CyberDW catalogue focusing on data ingestion, data quality, data tuning and performance of upstream cybersecurity data sources.
Indirect leadership and mentorship of junior team members.
Able to lead data feed development efforts and design initiatives.
Participate in development meetings to align development priorities and objectives, assign tasks, and share experiences and challenges with applications under development.
Consult with other technology and development teams as needed to coordinate on the integration of applications with the larger company software ecosystem.
Capture and document metadata for identified Key Data Elements (KDEs) to ensure accuracy and completeness for Data Quality (DQ) rules and processing of daily datasets.
Work with the data architecture team to align KDEs to the logical data models, develop physical data structures, and document physical data names, definitions, and data types.
Partner with the data owners and stakeholders to create technical requirements and DQ rules around the data elements needed in the CyberDW.
Partner with the Business Management team and Data Owners to understand what critical metrics and data fields are needed for Metric Dashboards.
Establish CyberDW views to encapsulate the data so that it is fit for downstream consumption.
Ensure that the data aligns with DQ rules established on that metadata, so it is fit for daily use.
Utilize IBM TWS production job scheduling system and adhere to standards around the daily scheduling and batch monitoring of production jobs.
Identify and resolve DQ issues including inaccuracies and incomplete information.
Enhance data quality efforts by implementing improved procedures and processes.