Roles & Responsibilities

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles & business goals
• Solve complex data problems to deliver insights that helps our business to achieve their goals
• Manage Databricks Spark Clusters and Delta lake requirements
• Responsible to build and select and test suitable models
• Deploys model and data pipelines using DevOps practices
• Responsible for Data enrichment & feature extraction.
• Implementation and deployment of emerging tools and process for analytic data engineering in order to improve our efficiency as a team
• Participates in multifunctional teams and makes insightful contributions
• Responsible for handling the MLops and data pipelines for ML models
• Training new resources on ML & Data engineering

Qualification:

• Academics: Bachelor’s in Computer Science, MIS, or Engineering preferred
• Prior Experience: Minimum 5+ years of experience in data or ML engineering role
• Experience with developing solutions on cloud computing services and infrastructure in the data and analytics space
• Experience in building robust data pipelines and deploying/maintaining them by applying DevOps principles
• Experience with developing solutions on cloud computing services and infrastructure in the data and analytics space (preferred)
• Experience with containerization and managing pipelines and microservices, e.g., Docker, Kubernetes, Airflow
• Experience or Understanding of Apache Spark or Databricks
• Expertise in programming Languages - Python , SQL
• Must have experience of working on GCP /e-com platforms particularly working with unstructured data
• Must have experience in running use cases in the sales analytics/ eCommerce domain

Technology Expertise

• SQL Implementation / or e-com platforms

Database development experience using BigQuery/Redshift and experience with a variety of relational, NoSQL, and cloud database technologies

• ML frameworks added advantages - Tensorflow, Pytorch..etc..
• Basic proficiency on ML techniques - clustering, decision tree learning, naive Bayes, support vector machines, Q-learning, temporal difference, adversarial networks, and neural networks
• Basic proficiency of data visualization techniques & tools - Visualizing complex insights using Plotly, matplotlib, ggplot2, etc; experience with dashboarding tools desired (e.g., Shiny, Dash, Tableau)
• Experience on Databricks is a plus.
• Python Data manipulation & Scraping libraries should be known (Beautiful soup, Scrapy..etc)

• Data Architecture