GCP Data Engineer

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.