Functional ownership in Finance Technology, specifically leading the implementation of Financial and Regulatory reporting functions onto the Corporate Technology Data Lake and leveraging Platform Services / Finance as a Service to perform data management.

More Uses of the Sqoop Toolkit:

  • Methodize: in order to beable to work in teams, as a big data environment is developed in a team of employees with different disciplines.
  • Support integration efforts of systems and data through application consolidation /migration/conversion, application integration, and data integration.
  • Assure your organization complies; plus exposure to software engineering techniques like micro services, event driven architecture, using event streaming technologies like Kafka.
  • Lead the full software development lifecycle (requirements, design, code, unit test, deployment, sustaining).
  • Make sure that your corporation analyzes highly complex business requirements, designs and writes technical specifications to design or redesign complex computer platforms and applications.
  • Manage stakeholder expectations with continuous engagement through status reports, proactive communication on new opportunities and issues.
  • Develop a community of practice for the proper execution of the architectural components and for the enablement of solutions architecture.
  • Manage work on finding cluster level solutions for your complex system and developed enterprise level applications followed by unit testing.
  • Steer: articulate the advantages or disadvantages of various big data technologies across a variety of client use cases.
  • Create source to target data mapping rules with the business transformation rules by considering with business users.
  • Engage with development team throughout the life cycle to help develop software for reliability and scale, ensuring minimal refactoring or changes.
  • Pilot: in order to beable to help program and project managers in the design, planning and governance of implementing projects of any kind.
  • Be accountable for troubleshooting complex system issues, handle multiple tasks simultaneously and translate user requirements into technical specifications.
  • Ensure you invent; build scalable data sets based on engineering specifications from the available raw data and derive business metrics/insights.
  • Warrant that your operation handles data manipulation (extract, load, transform), data visualization, and administration of data and systems securely and in accordance with enterprise data governance standards.
  • Head: implement extensively get involved in application migration from on prem to Cloud using automation procedures and continuous deployment procedures.
  • Oversee: design the solution taking advantage of the existing assets maintaining a balance between architecture requirements and specific client needs.
  • Collaborate with other team members (involved in the requirements gathering, testing, roll out and operations phases) to ensure seamless transitions.
  • Identify and recommend the most appropriate paradigms and technology choices for batch and real time scenarios.
  • Guide: design and build scalable data sets based on engineering specifications from the available raw data and derive business metrics/insights.
  • Innovate engaging with the customers business and technology stakeholders to create a compelling vision of a data driven enterprise in environment.
  • Engage with business partners and stakeholders across functions and assess current business and IT processes and work on generating RPA opportunities.
  • Identify data validation rules and alerts based on data publishing specifications for data integrity and anomaly detection.
  • Be certain that your corporation supports the build, maintenance and enhancements of data lake development; supports simple to medium complexity API, unstructured data parsing and streaming data ingestion.
  • Confirm your strategy complies; conducts the implementation and maintenance of complex business and enterprise data solutions to ensure successful deployment of released applications.
  • Organize: from virtualized telecommunications networks, big data and internet of things to mobile financial services, billing and operational support systems, you are continually evolving your business to help you become more connected.
  • Manage: design reusable data architecture and best practices to support batch/streaming ingestion, efficient batch, real time, and near real time integration/ETL, integrating quality rules, and structuring data for analytic consumption by end uses.
  • Provide visibility into the health of your data infrastructure (comprehensive view of data flow, resources usage, data lineage, etc).
  • Support the development, integration, and visualization of data science/machine learning algorithms for testing and operational deployment.
  • Devise: implement design and development phases innovative big data solutions to solve business problems across multiple client engagements.

 

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