Experience: 2 Years
Qualification: BS or MS in Computer Engineering, Computer Science
Key Responsibilities:
• Work with data science teams to implement required data flows from scratch, or making minimal modifications to existing data flows.
• Create and implementing checklists for new-product deployments (monitoring mechanisms, standardizing integration endpoints, and building support-handover instructions)
• Collaborating with external teams to validate and present data flow designs.
• Work with data scientists to build data flows that generate training/scoring/live data to data visualizations, insights, or machine learning models
• Work with client-facing teams to build data dictionaries or client inquiries
• SQL development as part of agile team workflow
• Real-time data pipelines and applications using serverless and managed AWS services such as Lambda, Kinesis, API Gateway, etc.
• Being part and contributing towards a strong team culture and ambition to be on the cutting edge of big data
• Evaluate and improve data quality by implementing test cases, alerts, and data quality safeguards
• Strong knowledge of cloud batch and stream processing architectures.
• Strong desire to learn.
Experience and Skill Set:
• Familiarity experience with GCP BigData Tooling: BigQuery, BigTable, etc
• Familiarity with data visualizations tools such as Power BI, Tableau, Qlik Sense, Data Studio etc.
• Familiarity with data lake concepts - Strong analytic skills related to working with unstructured datasets and combining data from a variety of heterogeneous data sources to provide actionable insights
• Ability to manage file operations, library configurations, and run scripts from shell (bash, zsh etc. )
• Knowledge of MS Office Suite – particularly Excel
• Strong communication skills—primarily English conversations.
• The ability to manage clients and internal stakeholders in ensuring that data is received and managed in timely and efficient manner
• Familiarity with relational data theory, ER models, DB technologies (e.g., NOSQL vs RDBMS)
• Familiarity with ad-hoc data analysis – SQL is a must and knowledge of R/Python/Scala data frames is preferable.
• Familiarity with task management tools, like JIRA, Trello etc.
• Strong problem-solving and critical thinking skills, and capability to adapt to new problems.