GCP Data Engineer

3 weeks ago

Bentonville, Arkansas, United States Cognizant Technology Solutions Full time

GCP Data Engineer.

Must have- GCP, Big Query, Cloud Composer, GCP File systems, GCP Hive.

Qualification:


• Post-grad in one of the following fields with strong academic credentials:

  • • Computer Science/IT.
  • • Operations Research/Applied Math.
  • • Engineering.
  • • Statistics.

Responsibility:

Business:

  • '
    •Works with the business team to identify the right business problem, gather the requirements and data required to answer the same.
  • •Data exploration , hypothesis testing and statistical modeling are part of daily activities.
  • •Involved in development , testing, evaluation and optimisation of models developed.
  • •Analyzes data and generates insights that can articulated to business stakeholders.
  • •Develops hypothesis for testing in consultation with Principal/Domain SME and Business teams.

Stakeholder Management:

  • '
    • POC for all the daily based activities and ensures the availabilty of all the required information with all the team at all the times.
  • '
    •Build the collaterals which are durable and reusable
    •Communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights.
  • • Coordinates in communicating the data needs with both technology and business teams to ensure that right data is captured for analysis and modeling.
  • 'Design qualitative & quantitative research instruments & methods (example: machine learning models, surveys, interviews etc) to capture the data if required.
  • Integrate qualitative & quantitative information to create insights.

Project Management:

  • '
    •Ensures that all the deliverables meets the delivery excellence standards and meets the stakeholders' expectations.
  • • Identifies risks to project execution and works with stakeholders to mitigate the same.
  • • Execute the design, analysis, or evaluation of assigned projects using sound engineering principles and adhering to business standards, practices, procedures, and product / program requirements.

Data Analytics and Reporting:

  • '
    • Explore and examine data from multiple disparate sources.
  • Prepare a data collection plan from both structured and unstructured sources.
  • Collaborate and coordinate with Technology and Business teams for all data needs.
  • • Expert level proficiency in data handling (SQL).

Data Discovery & Profiling:

  • '
    • Perform exploratory data analysis and generate insights.
  • Validate hypothesis developed during exploration phase.
  • Present initial results to business stakeholders and identify the next steps.
  • Design experiments with test and validate multiple hypothesis to meet/exceed expectations of customer due to the dynamic environment.

Data Modelling:

Create models using one or more of the platforms like R, SAS, Python, Matlab Model creation would involve one or more of the following technqiues:

  • 1 Classification.
  • 2 Clusterning, Segmentations.
  • 3 Time Series.
  • 4 Market Basket Anaysis.
  • 5 Text Mining(Structured and Unstructured Data).
  • 6 NLP, NLU, NLC.
  • 7 Decision Trees, RF.
  • 8 Network Analysis.
  • 9 Linear Programming.
  • 10 Optimisation.
  • 11 Deep Learning.
  • '
    • Testing and validating the model.
  • • Deriving insights and recommendations from the models.
  • • Performing data visualization and presentation to clients.

Innovation & Thought Leadership:

  • '
    • Provide thoughtleadership and dependable execution on diverse projects.
  • • implement best practices and technology.
  • • Discover new avenues by disecting the data and identify which all models can be utilised for a given business problem.
  • • Provide expertise thru PoCs and PoVs.

Knowledge Management:

  • 'Prepare a design, requirement document.
  • Document all modeling steps in a systematic way including modeling process, insights generated , presentations , model validation results and checklists built in the project.
  • Prepare a one pager document that outlines and quantifies the business impact due to the DS project.

People/Team Management:

  • ' Mentor a team of Data Scientists.
  • Set the timelines and monitor the progress of the project.
  • Ensure the timely delivery of deliverables and addresses the concerns related to tasks.
  • Understand aspirations of team members.
  • Set goals for team members and monitor performance.
  • Conduct appraisals.
  • Identify, priorities and deploy action items for competency development.
  • Guide the employee in setting career paths.

Must Have Skills

  • Machine Learning
  • SQL
  • Python
  • Supply Chain mgmt-Serv Retail

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