Senior Data Architect

NOTE: Must Have Banking or Financial Domain Experience.

Experience: 5 - 10 years

TECHNICAL SKILLS:

Must Have:

• Business Intelligence - Data Engineering
• DBT
• Snowflake
• SQL

Nice To Have:

• Data Visualization (preferably Power BI)
• Service Now development

GENERAL FUNCTION:

• The Data Engineer designs and builds data platforms, tools, and solutions that enable manage, secure, and extract value from its data.
• This role involves developing scalable, reusable solutions for collecting, storing, processing, and serving data across both traditional and modern architectures.
• Engineers work with technologies ranging from IBM DB2 and IBM Information Server (DataStage) to Snowflake and dbt, supporting both on-premise and cloud-based environments.
• The role also includes leveraging GenAI capabilities to boost productivity and prepare data for AI-readiness, while owning the full lifecycle of data assets to ensure quality, discoverability, and governance.
• Responsible and accountable for risk by openly exchanging ideas and opinions, elevating concerns, and personally following policies and procedures as defined.
• Accountable for always doing the right thing for customers and colleagues while ensuring that actions and behaviors drive a positive customer experience.
• While operating within the risk appetite, achieves results by consistently identifying, assessing, managing, monitoring, and reporting risks of all types.

ESSENTIAL DUTIES & RESPONSIBILITIES:

• Responsible for design, development, and support of scalable data solutions, APIs, tools, and processes that enable rapid delivery of business capabilities across both traditional and modern data platforms.
• Work closely with IT application teams, Enterprise architecture, infrastructure, information security, and LOB stakeholders to translate business and technical strategies into data-driven solutions.
• Act as a technical Expert addressing problems related to system and application design, performance, integration, security, etc.
• Conduct research and Development based on current trends and technologies related to the data engineering and architecture, data security, AI-readiness, and related topics.
• Build and maintain CI/CD pipelines and self-service deployment tools to streamline development and operations.
• Evaluate data and software products, providing documented recommendations to improve capabilities.
• Provide support and troubleshooting for data pipelines and platforms, including escalated on-call support for critical incidents.
• Participate in planning and execution of internal projects, including but not limited to, legacy system modernizations, modernizing data pipelines, monitoring, and documentation improvements.
• Optimize and automate data pipelines for efficiency, scalability, observability, and performance.
• Leverage GenAI tools to enhance engineering productivity and support the preparation of data for AI and machine learning use cases.
• Apply a data product mindset by managing the full lifecycle of data assets, ensuring quality, discoverability, and governance.
• Mentor and provide technical guidance to other team members.
• Manage and prioritize multiple assignments in a dynamic environment.
• Foster collaboration and continuous learning through cross-functional initiatives, mentoring, and knowledge sharing.
• Deliver high-impact data solutions in Agile teams through close collaboration with product owners and stakeholders.