Enterprise Data Architect

Responsibilities

• An expert to deliver a world class data architecture to an enterprise that establishes robust data governance and enforces best practices, policies, and procedures across the entire data landscape on cloud or premises.
• 15+ years of experience in designing data assets for extremely performant Operational Data Stores and near real time Data Warehouses. Well versed with industry standard Kimball and Inmon practices
• Expertise in creating ER (Entity Relationship), Logical, Physical, and Conceptual data models for an enterprise.
• At least 15 years hands on experience with Data Modeling tool such as Erwin. Data Modeling experience in financial industry is highly preferred.
• Drive reuse of data and data models at all levels and establish standards to enforce and preserve single version of truth across the data landscape.
• Solid communication skills and be able to facilitate / lead Data Modeling sessions with partners including Business, IT (engineers and developers), BI (Business Intelligence), System of Record (SOR) SMEs etc. to assess business requirements and deliver design prototypes and models.
• Experience in event streaming and event-based data warehousing methodologies.
• Experience in modern data design methodologies such as DDD (Domain Driven Design), Microservices Based Architecture, Bounded Contexts, and Data Mesh building data warehouses for agility, scalability, and autonomy.
• 10+ years of data analysis and profiling experience in financial services development, including account, card, payment, or digital processing.
• Expert SQL development, analysis, design, and performance skills in technologies such as MongoDB, Oracle EXADATA, Hadoop, Spark to support highly performant physical data models.
• Expert hands-on development, and performance tuning experience in Snowflake including writing pipelines, coding stored procedures, and establishing data share.
• Well versed in Power Point and be able to design complex dashboards in Power BI.
• Expert in Excel and Splunk with experience in writing complex macros to expedite data analysis.
• Experience in reverse/forward engineering analysis for complex data flows and be able to develop source-to-target and process mappings.
• Strong scripting experience in Python to create alerts and monitoring, build ingestion and interfaces to move data between systems and automate data governance.
• Experience in building and maintaining an evergreen Data Catalogue for an enterprise with robust metadata and end to end lineage. Experience with Alation, Collibra etc. is nice to have.
• Experience in tools such as Matillion, SnapLogic, or DBT is nice to have to be able to build data pipelines for on cloud / on premises databases such as Snowflake.
• Experience with industry reference frameworks such as BIAN (Banking Industry Architecture Network), IBM Banking & Financial Markets Model, ISO20022 is a plus.

Qualifications

• Excellent interpersonal, written, and verbal communication skills – able to break down complex information and articulate in a manner that is easy to understand.
• Comfortable working in a fast paced, highly complex, matrixed environment and capable of handling multiple tasks concurrently.
• Be able to understand current data landscape, interpret results of analysis, identify trends and issues, and develop data standards to support business objectives.
• Ability to exercise independent judgment and creative problem-solving techniques for data solutions in a large and complex environment.
• Strong organizational skills with demonstrated ability to work with partners to meet business SLAs and plan and deliver work to expectations.
• Functional knowledge of the Financial Services industry with experience promoting a robust risk & control environment is preferred.
• Be able to coach, train, and mentor junior staff.