Data Science & Analytics Services
- Apache Airflow
- Apache Spark
- Google BigQuery
- AWS SageMaker
- Azure ML Studio
- Power BI
- AWS Quicksight
- Google Data Studio
- Amazon Web Services
- Google Cloud Platform
- Azure Cloud Service
- Data Warehousing & Data Lake: Building Data lakes, Data Warehouse and Data Lakehouse on AWS (Amazon Web Services) and GCP (Google Cloud Platform).
- Data Wrangling: This involves processing the data in various formats, analyzes and get them to be used with another set of data and bringing them together into valuable insights.
- Data Engineering: Building ETL & ELT pipelines from various data sources and building data workflows.
- Data Analytics: Helping clients with descriptive, predictive and prescriptive data analysis and helping them to identify root cause.
- Machine Learning: Building efficient ML models for predictive analysis.
- Cloud & Solution Architecture: Building end-to-end solutions on AWS (Amazon Web Services) and Microsoft Azure.
- Reporting and Dashboard building: Building OLAP reporting and OLTP reporting on Power BI, Tableau and SSRS (SQL Server Reporting Services).
Achievements & Milestones/ Deployments
- Building an Enterprise data lakehouse on AWS (Amazon Web Services) for an SME client.
- Data Visualisation for Enterprises.
- Data Engineering on SAP HANA and setting up reporting architecture on Power BI for Business Insights for an Enterprise.
- Data Analytics for Enterprises.
- Worked on Data Warehousing for Business Insights and wrote multiple Stored Procedures.
- Building Reporting System for Startups with applying ETL processes on Metabase.
- Data warehouse, Business Intelligence and Data Mining project for an Enterprise.
- Consulting for Enterprise to build MLOps and Data Engineering Platform.