SUMMARY

• Effectively envisions, designs, analyzes, implements and supports business level information processes and applications in a complex, multi - tiered architecture across diverse platforms and teams. An innovative, adaptable and visionary problem-solver willing and able to quickly learn new concepts and technologies.
• An innovative, adaptable and visionary problem-solver willing and able to quickly learn new concepts and technologies. Frequently serves as liaison between designers, customers and infrastructure personnel in delivering quality, cost-effective, strategic-but-practical solutions to the business.
• Data Design/Modeling: OLTP ( Normalization), OLAP, Big Data, EDW (Star Schema and Snow Flake Schema), Data Lake, Erwin Data Modeler(tool), Power Designer, Data Mapping Source to Target.

PROFESSIONAL EXPERIENCE

Confidential

Sr. Data Modeler

Responsibilities:

• Defined and lead the delivery of data architectures to support both operational and analytical needs.
• Defined optimal data integration solutions and patterns to meet business requirements
• Defined and assisted the Enterprise Architecture team in the development of future state information architecture strategy.
• Support the implementation of data strategy, future state architecture through architecture solutions on projects
• Guide product teams in the design and delivery of various data products
• Extensively worked Data warehouse and Business Intelligence Health analytics
• Designed Conceptual, Logical & Physical data models with various data design patterns such as OLTP, ODS, OLAP, EDW, data marts and Big Data.
• Experienced with various forms multiple data warehousing methodologies and modeling techniques
• Working knowledge of ETL design, SQL, Hadoop, HIVE and BI tools
• Working knowledge of next generation data platforms including Streaming technologies, Spark, NoSQL, Graph, Data Virtualization, and self-service data platforms
• Understands all domains of architecture and applies that knowledge to create data solution architectures to solve complex business problems that have a broad impact on the business
• Experience defining and negotiating solution options while considering risks, costs, and impact on the enterprise business processes and goals
• Good interpersonal skills, including teamwork, facilitation and communication

Confidential

Sr. Data modeler / Data Governance Analyst

Responsibilities:

• Facilitate interview sessions with business stakeholders to understand, define and business requirements gathering, data capturing, Business data quality rules and technical quality rules and analysis, and business rule identification
• Engaged in tool evaluations from a data management perspective
• Created data governance strategies and deploy recommendations and roadmap
• Lead the data Governance Working Group facilitated sessions and discussions, track and follow up on action items, records and distribute meeting minutes
• Ensure that the policies and processes created are carried out
• Coordinate with the Data Governance Working Group
• Train the Data Governance Working Group in best practices in data quality, data management, and data definitions
• Communicate Data Governance direction, coordinate and demonstrate Data Governance depth in discussions with the client
• Define a current state discovery analysis and needs assessment
• Provide gap analysis from a data governance perspective, highlighting current state, future state, client needs, best practices, and solution offerings
• Developed roadmap/methodology required to move from current state to future state
• Provided expert knowledge of enterprise Data Governance strategies and best practices
• Convene subgroups of the Data Governance work groups to execute prioritized data governance activities and to address critical data issues
• Identify potential areas where existing policies, processes and standards require change or where new ones need to be developed to ensure Data Governance objectives are met
• Facilitate change management processes and tasks
• Lead/supported project management activities and report on project status, risks and issues
• Provided expert knowledge in Data Governance, Data Stewardship, Data Quality, Master Data Management and Data Warehousing
• Ability to facilitate small group (up to 20) discussion and decision making
• Ability to navigate and work effectively in a dynamic environment, maintaining strong relationships across functional teams
• Contributed to the development and maintenance of metadata strategy and standards.
• Facilitated consistent adoption of metadata standards across the enterprise, demonstrating the value added by adoption.
• Defined and revise data taxonomy, including identifying and managing standard list of data domains and managing domain definition conflicts
• Lead the implementation & rollout of metadata platform (MITI, IBM Infosphere, Collibra)
• Collaborate with business & technical stakeholders on maintaining metadata repository.
• Support metadata distribution & usage across the enterprise.
• Advocate data use and protection practices, including consent and identity management. Receiving, tracking, and taking action on requests to and from stakeholders concerning policies and procedures, e.g., regulatory inquiries, audits, data advisory board reviews, consent management, incident response, Cross functional analysis and business continuity

Sr. Data Modeler

Responsibilities:

• Design Operational, Data warehouse and Data Lake plans for a group of finance products.
• Gathering business requirements for database and application updates/changes. Troubleshoot complex data, features, personalization patient offer build rules issues and perform root cause analysis to proactively resolve product and operational issues
• Design the conceptual, logical and physical data models. Developing, validating, and communicating OLTP & OLAP relational data modeling solutions.
• Using CA ERwin to design data models; publishing the models to CA Erwin Web Portal tool to view the metadata information about the tables and lineage between the data elements.
• Connect business context and perspective to define model objective functions, features, business rules, prioritization, measurement.
• Lead conversations with infrastructure teams (on - prem & cloud) on analytics application requirements (e.g., configuration, access, tools, services, compute capacity, etc
• Implementing metadata strategy and building metadata repository using Erwin while maintaining business rules, data dictionaries, data transformation rules.
• Generating DDL scripts and providing them to the database administrators to physically implement on the database server.
• Working on data warehouse, database and Operational Data Storage (ODS), and data marts; creating meta data repository of various data elements in the data warehouse and data marts. Demonstrates the ability to provide technical leadership on large complex data integration projects
• Demonstrates ability to balance tactical delivery with strategic vision and can articulate the tradeoffs and recommendations as needed.
• Understands all domains of architecture and applies that knowledge to create data solution architectures to solve complex business problems that have a broad impact on the business. Experience in defining and negotiating solution options
• Involved in creating the source-to-target mapping document and end-to-end document.
• Creating templates, domains and naming standards for designing the data models. Managing the model mart efficiently to keep all versions of models up to date. Working with the ETL team by providing required documents and generating business reports.
• Working on data cleansing and data profiling. Working with the ILG (Information Lifecycle Governance) for newly created databases and tables in classifying them for the internal purge and archival process of data.
• Creating meta data repository of the various data elements in the data warehouse and data marts.
• Experience working via an agile, sprint-based working style Experience working side-by-side with business owners, and translating business needs into analytics solutions
• Proven ability to successfully balance near-term results (e.g., ability to design and execute on a MVP model), with long-term goals