Azure Data Analytics

.

.

AZAS MOHAMMAD

Morrisville, North Carolina Azas LinkedIn Gmail:****.****@*****.*** Ph no: 469-***-**** SUMMARY

Results-driven Data Analytics Engineer with 5+ years of expertise in designing and implementing scalable data solutions using SQL, Python, PySpark, Airflow, and Hive. Skilled in building robust ETL pipelines, managing large datasets, and leveraging cloud platforms such as Azure and AWS for data processing and storage. Demonstrated success in optimizing operational workflows, reducing costs, and delivering actionable insights through advanced analytics and data visualization. Proficient in developing end-to-end data pipelines and driving strategic growth initiatives through comprehensive data analysis.

SKILLS

Programming Languages: SQL, R, Python, Java, PySpark. Visualization Tools: Tableau, Power BI.

Databases: PostgreSQL, Aurora, MySQL.

Datawarehouse: Teradata, Snowflake, AWS Redshift, Big Query, Azure SQL warehouse, IBM Netezza. Cloud Platforms & Services:

Azure: Azure Data Lake, Blob Storage, Azure Functions, Azure Data Factory, Azure Databricks, Azure SQL Datawarehouse, Azure HDInsight, Azure Event Hub, Azure Event bridge. Aws: AWS Data Lake, AWS S3, Lambda Function, Glue, RedShift, EMR, Dynamo DB, SNS, SQS, Cloud watch, Cloud Trail, Cloud Formation, AWS Quick sight, AWS MWAA, AWS Databricks. CERTIFICATIONS

Microsoft Azure : Azure Data Engineer Associate

Tableau Certified: Tableau Desktop Specialist

Google Certified: Google Associate Cloud Engineer

EXPERIENCE

S&P Global Market Intelligence

Senior Data Analyst Mar 2021 – Jan 2023

• Designed and implemented scalable ETL pipelines using Spark on AWS Databricks to enrich and load data into AWS Data Lake.

• Authored PySpark scripts and UDFs to transform and process large datasets in AWS Databricks.

• Built ETL processes in AWS Glue and Python to migrate campaign data from S3 in formats like Parquet, ORC, and text files to AWS Redshift.

• Developed data ingestion pipelines on AWS EMR Spark clusters using Spark SQL and integrated with DynamoDB.

• Created robust ETL pipelines in Azure Data Factory to integrate on-premises and cloud data, transforming it using Spark for Azure SQL Data Warehouse.

• Designed ad-hoc tables to structure and validate data in AWS S3 using Lambda functions, performing transformations for DynamoDB changes and loading results into PostgreSQL.

• Configured AWS Redshift clusters and spectrums for seamless querying, data sharing, and inter-cluster transfers.

• Set up alarms and notifications for EC2 instances using CloudWatch, CloudTrail, and SNS.

• Utilized Spark Streaming API for real-time data ingestion from various sources, storing transformed data in Azure Table and optimizing PySpark code for enhanced performance.

• Developed Azure Data Factory pipelines to extract, transform, and load data from multiple sources, including Azure SQL and Blob Storage.

• Configured Snow pipe to ingest data from S3 buckets into Snowflake's staging area and implemented micro- batching for processing large file volumes.

• Authored advanced SQL queries and created interactive AWS Quick Sight dashboards for actionable business insights.

.

.

• Implemented Dimensional Data Modeling to deliver Multi-Dimensional STAR schemas and Developed Snowflake Schemas by normalizing the dimension tables as appropriate.

• Developed custom-built input adapters using PySpark and ingested the enriched data to snowflake Datawarehouse.

Airtel Telecommunication

Data Analytics Engineer Jan 2018 - Mar 2021

• Created 12 customer segments for targeted marketing, enhancing campaign precision and maximizing engagement through detailed segmentation analysis.

• Designed 15 Tableau dashboards to visualize metrics such as net sales, average sales, store performance, discounts, competitor pricing, and regional sales, providing actionable business insights.

• Configured and managed AWS S3 buckets and Glacier for secure data storage and backup.

• Built Apache Airflow DAGs to automate data export to AWS S3 buckets, triggering AWS Lambda functions for seamless workflows.

• Developed scalable data integration pipelines to migrate data from S3 to Redshift using Python and AWS Glue, ensuring efficient data processing.

• Implemented real-time data ingestion and analytics pipelines using Spark, integrating AWS Lambda for dynamic monitoring dashboards.

• Designed dashboards and reports in Tableau to analyze POS data and conducted ad-hoc reporting to meet business needs.

• Partnered with operations to create 20 live dashboards for query log monitoring, enhancing customer support efficiency.

• Analyzed agent call time gaps to optimize peak-time management, reducing response times by 30% and improving customer satisfaction.

• Automated Tableau dashboards to calculate minimum expected penetration based on pin codes, increasing accessibility and reducing access time by 50%.

• Collaborated with the network expansion team to define KPIs for launching 10 new stores in strategic locations, driving organizational growth.

• Conducted cohort analysis in Excel using advanced formulas, Power Pivot, and macros, improving customer retention by 15%.

• Developed advanced SQL functions in IBM Netezza for extracting cohort data, supporting strategy development with comprehensive datasets.

PROJECTS

House Sales Dashboard (Kaggle)

Texas A&M University Commerce

• Engineered highly accurate machine learning models achieving 92% accuracy in predicting house prices, utilizing diverse features for precise sales price estimations.

• Conducted comprehensive data exploration and preparation, effectively 98% of missing values, and significantly enhancing data quality through rigorous cleaning process.

• Employed a variety of machine learning techniques, including generalized linear models, ensemble methods and neural networks, to fine -tune predictive accuracy for housing market analysis. EDUCATION

Texas A&M University Commerce Jan 2023 - Aug 2024

Master Of Science in Business Analytics

Contact this candidate