Avinash Kumar

• Explained intricate data analysis and modeling concepts to
non-technical stakeholders for data-driven decision-making.
• Developed and implemented a change management plan,
ensuring minimal disruption during organizational changes.
• Communicated data experiment outcomes effectively to both
technical and non-technical audiences.
• Worked on NoSQL databases MongoDB and Spark for data
modeling, utilizing reverse engineering for model creation and
documentation.
• Used NoSQL booster for MongoDB to analyze, profile, map, test,
and validate data during reverse engineering.
• Converted JSON strings to native objects. Designed and
developed NoSQL database models in Cassandra and
DynamoDB, and relational models using Erwin.
• Transformed monolithic data models into scalable, domain
driven models. Developed applications and migrated data
from RDBMS to NoSQL databases like Cassandra.
• Used Cassandra stress tool for read-write latency testing and
cluster fine-tuning, maintaining data dictionary and global
maintenance processes.

<p>Explained intricate data analysis and modeling concepts to non-technical stakeholders for data-driven decision-making.<br>
Developed and implemented a change management plan, ensuring minimal disruption during organizational changes.<br>
Communicated data experiment outcomes effectively to both technical and non-technical audiences.<br>
Worked on NoSQL databases MongoDB and Spark for data modeling, utilizing reverse engineering for model creation and documentation.<br>
Used NoSQL Booster for MongoDB to analyze, profile, map, test, and validate data during reverse engineering.<br>
Converted JSON strings to native objects.<br>
Designed and developed NoSQL database models in Cassandra and DynamoDB, and relational models using Erwin.<br>
Transformed monolithic data models into scalable, domain-driven models.<br>
Developed applications and migrated data from RDBMS to NoSQL databases like Cassandra.<br>
Used Cassandra stress tool for read-write latency testing and cluster fine-tuning, maintaining data dictionary and global maintenance processes.</p>

<p>Translated business requirements into functional and non-functional requirements using Workflow, Sequence, Activity Diagrams, and Use Case Modelling.<br>
Created Physical and Logical models using Erwin, and generated CSV reports and DDL.<br>
Built data models in Erwin, and designed grain of facts based on reporting needs.<br>
Conducted data analysis, identifying data sets, sources, metadata, definitions, and formats.<br>
Operated on AWS cloud platform (EC2, S3, EMR, Redshift, Lambda, Glue).<br>
Defined and tracked KPIs and metrics for data governance effectiveness and improvement.<br>
Trained personnel in relational database use and Data Governance policy/procedures.<br>
Implemented and maintained data governance frameworks (e.g., DAMA-DMBOK).<br>
Enforced referential integrity in the OLTP data model for consistent table relationships.<br>
Analyzed system specifications/business requirements, ensuring compliance with corporate rules and regulations.</p>

<p>Successfully migrated on-premises data to GCP using Azure Migrate tools for scalability and cost-effectiveness.<br>
Conducted thorough analysis of client's data architecture, implementing data governance policies compliant with regulations.<br>
Utilized Azure Data Factory to orchestrate data migration from Azure SQL databases and Blob storage to GCP's BigQuery and Cloud Storage.<br>
Configured secure network connectivity between Azure and GCP via VPC peering and VPN gateways.<br>
Used GCP Transfer Service to simplify migration, transferring large data volumes from Azure Blob storage to Google Cloud Storage.<br>
Leveraged Snowflake's data warehousing for optimized data storage and retrieval, enhancing accessibility.<br>
Implemented Azure Cognitive Services, increasing document processing efficiency by 25%.<br>
Enhanced document intelligence through Azure Forms Designer and Custom Vision, achieving a 20% accuracy boost.<br>
Worked extensively in Data Governance, focusing on metadata management, master data management, data quality, and security.<br>
Led cross-functional teams, prioritizing transparency and efficiency through agile methodologies, developing roadmaps and managing budgets.</p>

<p>Utilized Azure Data Factory and Azure Databricks to streamline data audit trails, reducing platform usage costs by 15%.<br>
Constructed episodes using Medicare Advantage claims data with Python, SQL, and Azure Synapse Analytics, increasing program enrollment by 10%.<br>
Applied data intelligence techniques on Azure, resulting in a 30% surge in BPCI-Advanced program enrollment.<br>
Optimized ETL data pipelines in Apache Airflow on Azure, boosting data processing efficiency by 60%.<br>
Developed ETL workflows with Azure Data Factory, loading diverse datasets into Azure SQL Data Warehouse.<br>
Collaborated on Airflow-based data solutions on Azure, aligning with business needs and best practices.<br>
Integrated Azure Functions in Airflow to enhance ETL job orchestration and data pipeline optimization.<br>
Designed and implemented over 50 Airflow DAGs using Azure Functions for efficient ETL workflow management.<br>
Developed CI/CD workflows with Jenkins and Azure Functions, improving deployment speed and reducing manual errors.<br>
Crafted custom Airflow operators and sensors on Azure, enhancing development efficiency and data processing accuracy.</p>

<p>Defined project scope, gathered business requirements, and performed GAP analysis.<br>
Implemented Data Lake using Hadoop architecture.<br>
Loaded data into Hive tables from HDFS to enable SQL access to Hadoop data.<br>
Created MDM rules using IBM Infosphere Master Data Management.<br>
Developed logical data models with IBM Infosphere Data Architect.<br>
Converted user requirements into business, functional, and technical specifications, and managed requirements using DOORS.<br>
Defined business logic for web services in SOA-based applications.<br>
Created physical data designs and first-cut data models for various projects.<br>
Performed performance tuning using IBM Infosphere DataStage 8.5.<br>
Worked with diverse data formats and developed ETL strategies for data warehouse projects, including running Hadoop streaming jobs and using Flume for data collection.</p>

<p>Identified core systems' processes, workflows, and data flows.<br>
Conducted extensive user requirements gathering and gap analysis.<br>
Participated in the full development cycle: Planning, Analysis, Design, Development, Testing, and Implementation.<br>
Developed PL/SQL triggers and master tables for automatic primary key creation.<br>
Conducted data analysis for data conversion, including data mapping, specification, and writing data extract scripts.<br>
Created advanced PL/SQL packages, procedures, triggers, functions, indexes, and collections for business logic implementation using SQL Navigator.<br>
Built ETL packages using SQL Server SSIS, extracting data from XML files and loading it into databases.<br>
Designed and developed Oracle forms and reports, generating up to 60 reports.<br>
Performed data loading and extraction using SQL*Loader.<br>
Administered all database objects, including tables, clusters, indexes, views, sequences, packages, and procedures, and extensively used exception handling for debugging and error messaging.</p>