Azure Data Engineer

Nandan Kumar Kyasa

Sr. Azure Data Engineer

Phone: +1-859-***-****

Mail: ******************@*****.***

LinkedIn: https://www.linkedin.com/in/nandan-kumar-kyasa/

PROFESSIONAL SUMMARY:

• Azure Data Engineer Associate Professional with over 11+ years of comprehensive IT experience, including 6+ years in Azure Cloud Development, specializing as an Azure Big Data Developer, and 4 years as an Azure Data Warehouse Developer.

• Proficient in all phases of Azure Data Processing System Analysis, Architecture, Design, Development, and Implementation, ensuring efficient and scalable data solutions in cloud environments.

• Over 6+ years of expertise in developing Azure ETL Data Pipelines, proficient in Java/PySpark on Azure Databricks.

• Demonstrated capabilities in designing, building, and maintaining high-availability Azure Databricks clusters, optimizing cluster pools with Azure Data Lake Storage Gen2, ensuring resilient and efficient data processing.

• Extensive hands-on expertise in Azure Data Conversion, leveraging Azure Cloud and its core components such as Azure Data Factory (ADF), Azure HD Insights, Azure Data Explorer, Azure Event Hub, Azure Databricks, Azure Logic Apps, and Azure Cosmos DB.

• Hands-on experience with Data Flow transformations and trigger implementations in Azure, optimizing data processing workflows within Azure Data Factory (ADF).

• Expertise in Data Wrangling with tools like PySpark, Azure Data Lake, and Databricks to clean, transform, and process large datasets.

• Extensive experience with Azure Data Governance, applying robust strategies for secure access control and compliance via Azure Purview.

• Seamlessly integrate and convert legacy data systems into modern, cloud-based solutions. Ensure efficient data pipeline create, transform, and automate for enhanced data accessibility.

• In-depth experience in building and developing Azure Ingestion Patterns, utilizing Azure Data Factory (ADF) along with Azure Integration Runtimes, Azure Databricks, Azure Key Vault, Azure Logic Apps, Azure Data Lake (Blob, ADLS Gen1 & Gen2), Azure Virtual Machines (VMs), and Azure DevOps Git Repos.

• Ensure efficient data pipeline creation, transformation, and automation for enhanced data accessibility and processing.

• Deep understanding of Azure Delta Lake, working extensively with Azure Delta Tables, Azure Unity Catalog, Azure Time Travel, Azure Delta Sharing, Azure Auto-loader, and Azure Delta Live Tables.

• Highly proficient in processing large structured, semi-structured, and unstructured datasets to support complex Azure Big Data applications, ensuring smooth and efficient data storage, retrieval, and transformations.

• Expertise in Azure Data Streaming, utilizing Azure Event Hubs, Azure Hive, and Azure Spark Structured Streaming for building scalable and reliable data pipelines.

• Handle data ingestion, transformation, and analysis in real time, crafting data-driven solutions capable of processing massive datasets with minimal latency.

• Strong command over executing Azure Hive scripts on Azure Spark, implementing Azure Hive Partitioning, Azure Hive Bucketing, and performing various Azure Hive Joins.

• Optimize data queries and enhance performance across Azure-based big data environments. Proficient in using Azure Spark Core and writing optimized Azure Spark SQL scripts in Python to accelerate data processing.

• Working knowledge of Azure-supported file formats (Parquet, ORCFILE, TextFile) and Azure Compression Codecs (GZIP, BZIP2, SNAPPY, LZO).

• Ensure effective storage and retrieval of data while maintaining data integrity and minimizing data storage costs. Streamline data workflows and optimize the performance of enterprise data solutions.

• Well-versed in Azure-based Data Warehousing solutions, leveraging Azure Snowflake Multi-Cluster Warehouses for scalable and cost-efficient data processing.

• Advanced proficiency with Azure Snowflake, optimizing Azure Snow-SQL for enhanced cost efficiency and performance. In-depth experience with Azure Snowflake utilities, Snow Park, Snow Pipe, Snow SQL Tasks, Stored Procedures, and Azure Snowflake Integration Objects.

• Implementing Azure-based Master Data Management (MDM) platforms, including Azure Reltio MDM, to ensure high-quality data governance.

• Facilitating the efficient extraction and loading of data to support comprehensive Azure Data Governance and Integration Processes, driving quality and consistency in enterprise data systems.

• Hands-on expertise in Azure Data Warehousing and Azure Business Intelligence, specifically focusing on Azure ETL Development using tools like Azure Informatica PowerCenter and Azure IICS.

• Design, optimize, and manage ETL workflows while improving the quality, accessibility, and security of enterprise data assets. Extensive experience with Azure Data Governance, applying robust strategies for secure access control and compliance via Azure Purview.

• Proficient in managing data quality, lineage, and ensuring data security, meeting regulatory requirements while enabling reliable data access across enterprise platforms.

• In-depth experience working with Azure Synapse for big data analytics, utilizing its scalable architecture to build, optimize, and manage complex data pipelines.

• Efficiently managing large datasets for advanced analytics, reporting, and business intelligence.

• Proficient in building Power BI reports and dashboards for data visualization, empowering stakeholders with actionable insights from complex datasets.

• Expertise in working with Azure Synapse and Azure Data Lake (Blob, ADLS) to generate real-time insights and key business metrics. Manage large-scale datasets for efficient querying and analysis.

• Skilled in data wrangling using Azure Databricks and Azure Synapse, enabling seamless data processing and transformation for various business applications.

• Highly proficient in designing and developing OLAP and OLTP solutions in Azure SQL, ensuring data integrity and optimized performance across relational databases (DBT).

• Ensure the smooth execution of ETL processes, enhancing the overall efficiency of data management operations. Enable high-quality data for downstream analytics and machine learning tasks.

• Implemented Zero Copy Cloning in Delta Lake, improving data storage and access efficiency across multiple datasets within Azure environments.

• Reduce storage costs while providing a fast, reliable method for managing data changes.

• Developed and maintained Azure Data Pipelines, optimizing both batch and real-time data streaming solutions with tools like Azure Event Hubs, Azure Functions, and Azure Databricks.

• Ensure data is ingested, transformed, and made available for analysis in near real-time. Ensure smooth deployment and high-availability production workflows across Azure-based environments.

• Proficient in Azure CI/CD Pipeline Implementation, utilizing Azure Jenkins to automate deployment, monitoring, and testing processes.

• A collaborative Azure Data Engineer, consistently ensuring the stability and integrity of Azure Data Pipelines while playing an active role in Azure-based ETL tasks.

• Skilled in building scalable ETL/ELT pipelines, data governance, and AI-driven analytics solutions using tools such as Azure Data Factory, Databricks, Snowflake, and Apache Airflow.

• Experienced in collaborating with stakeholders to drive business value through personalized recommendations, demand forecasting, and inventory optimization.

• Results-driven Senior Azure Data Engineer with extensive experience in designing, developing, and optimizing data solutions on cloud platforms, particularly Azure.

• Adept at migrating on-premise DBT databases to Azure, implementing CI/CD pipelines, and ensuring data security with Microsoft Purview.

• Skilled in utilizing Azure JIRA and Azure Notion for effective project management and reporting, leveraging version control tools like Azure SVN, Azure Git, and Azure Bitbucket to ensure seamless collaboration across Agile Methodologies.

• Active participation in Azure Agile Ceremonies, including Daily Stand-ups and Program Increment (PI) Planning sessions.

• Demonstrate effective project management skills and a collaborative approach to meeting team goals.

• A key player in driving team performance and achieving project deliverables within Azure Agile environments.

• Proficient in managing data quality, lineage, and ensuring data security, meeting regulatory requirements while enabling reliable data access across enterprise platforms.

• Proficient in Agile (Scrum, Kanban) and Waterfall methodologies, managing projects through sprint planning, backlog refinement, and iterative development.

• Ensure timely delivery of high-quality data solutions, with strong leadership experience in mentoring junior engineers and driving cross-functional collaboration within an Agile framework to align data strategies with business objectives.

TECHNICAL SKILLS:

Cloud Technologies:

Azure Data Factory (ADF), Azure Blob Storage, Azure Data Lake Storage (ADLS), Azure Data Bricks, SQL DB, Azure Synapse Analytics, Azure Data Lake, Azure Logic Apps, Function Apps, Azure Event Hubs, Azure ML, Azure Kubernetes Service (AKS), MLflow, Azure Delta Lake, Purview, Entra ID/Active Directory.

Big Data Ecosystems:

Hadoop (1.0X and 2.0X), Hortonworks HDP (2.4/2.6), Mapr Distribution (5.0/6.0), HDF, YARN, MapReduce, Pig, HBase, Hive, Sqoop, Flume, Spark, Map Control Systems, Apache Kafka, Splunk, Elastic Search.

ETL Tools:

Data Build Tool (DBT), IBM Information Server 11.5/9.1/8.7/8.5, IBM Infosphere DataStage 7.5.X, Informatica PowerCenter, SSIS, SSRS.

Data Modeling:

Data Modeling, Star Schema Modeling, Snow-Flake Schema Modeling, FACT and Dimensions Tables, DAX, Erwin 4.0/3.5, Slowly Changing Dimensions (SCD), Change Data Capture (CDC).

Programming:

MapReduce, PIG, Java, PySpark, Python, Scala, Linux, Unix Shell Scripting, SQL, PL/SQL.

ML Libraries:

Pandas, Scikit-Learn, Matplotlib, Pytorch, ML Flow, Azure ML.

Databases:

HBase, Mongo DB, Cassandra, Oracle 11g/10g, MS SQL Server, MySQL, Teradata V2R5/V2R6, DB2, PostgreSQL, Cosmos DB, Netezza, HP Vertica.

Business Intelligence:

Power BI, Qlik Sense, Tableau, DAX.

Scheduling:

Control-M, Autosys, Oozie, Apache Airflow.

Version Control and CI/CD Platform:

Git, GitHub, GitLab CI/CD, Azure DevOps, ARM Templates, Jenkins, Docker.

EDUCATIONAL QUALIFICATIONS:

• Bachelors –Jawaharlal Nehru Technological University, Hyderabad, Telangana, India, Jun 2008 – May 2012.

CERTIFICATIONS:

• Dp-203: Azure Data Engineer Associate

• Databricks certified Data Engineer associate

• SnowPro® Core Certification

PROFESSIONAL EXPERIENCE:

Sr. Azure Data Engineer Oct 2021- Current

Starbucks, Seattle, WA

Responsibilities:

• Built Azure-based data pipelines for customer analytics, transactional data processing, and operational efficiency using Python, PySpark and SQL.

• Optimized Databricks Delta Engine for customer engagement models and promotional strategies using Python-based transformations and DBT models for version-controlled data workflows.

• Designed and developed scalable BI solutions on Azure Cloud using Azure Databricks, ADLS, Azure Synapse, Azure Stream Analytics, and Azure Cosmos DB for enterprise-wide data integration and analytics.

• Implemented Medallion Architecture (Bronze, Silver, Gold layers) in Databricks Delta Lake, ensuring structured, high-quality data transformation for enterprise analytics and reporting.

• Built Bronze layer pipelines for raw data ingestion from various sources, ensuring efficient batch and streaming data integration using Azure Data Factory and Databricks Auto Loader.

• Developed Silver layer transformations for data deduplication, validation, and enrichment, leveraging Azure Databricks, PySpark, DBT and Delta Lake.

• Optimized Gold layer datasets for BI reporting, AI-driven insights, and interactive dashboards in Power BI, Looker, Tableau, and Azure Synapse SQL Warehouse.

• Leveraged Power BI and Tableau for interactive reporting, integrating with Azure Databricks SQL Warehouse and ADF for real-time and historical analytics.

• Utilized data modeling techniques for OLTP and OLAP workloads in Snowflake and Azure Databricks, ensuring data consistency with Databricks Delta Lake, DBT Transformations and Looker semantic modeling.

• Orchestrated AI-driven customer feedback analytics using Azure Cognitive Services, AKS, and Python-based NLP models in Azure Databricks.

• Enabled cost-effective, highly available, and scalable data processing for customer transactions, sales forecasting, and loyalty analytics with Databricks-powered Python transformations — reducing processing cost by 30%.

• Developed ETL pipelines in ADF integrating ADLS, Snowflake, and Databricks, implementing Linked Services, Datasets, Pipelines, and Data Flows.

• Built CI/CD pipelines for ADF, Databricks, and Snowflake using Git, GitHub, and Python automation, ensuring seamless integration of new workflows — improved deployment speed by 40%.

• Automated real-time and batch data processing in Azure Databricks Delta Live Tables (DLT) and Lakehouse Federation, aligning with Medallion Architecture best practices.

• Implemented Unity Catalog in Databricks for metadata management, security policies, and access control across global business units.

• Implemented Delta Live Tables (DLT) in Databricks to build automated, reliable, and scalable data pipelines, significantly reducing pipeline maintenance overhead and improving data freshness for customer behavior analytics.

• Optimized real-time data processing by leveraging Delta Live Tables for streaming ingestion and transformation of operational data, enabling near real-time insights for supply chain monitoring and inventory forecasting.

• Developed scalable Spark applications in Databricks using Python, PySpark, SQL, and Java for high-volume transactional data analytics — improved query performance by 25%.

• Processed multiple data formats (Parquet, CSV, JSON) and integrated structured outputs into Salesforce CRM. Secured data with Azure Key Vault and Databricks secret scopes.

• Leveraged Microsoft Fabric to unify data engineering workflows, enabling seamless integration of data movement, transformation, and visualization across OneLake and Power BI, leading to faster decision-making and simplified data architecture.

• Developed end-to-end data pipelines using Microsoft Fabric's Data Factory and Lakehouse components, orchestrating ingestion from diverse sources (including on-prem, cloud, and APIs) into a governed, enterprise-ready lakehouse for customer analytics.

• Designed real-time data streaming architectures using Azure Event Hubs, Azure Stream Analytics, and Azure Functions, integrating Databricks Streaming for fraud detection and marketing campaigns.

• Led on-prem to Azure Cloud migration using ADF with Self-Hosted Integration Runtime and Databricks Auto Loader for data ingestion.

• Designed and maintained Apache Airflow DAGs for ETL orchestration, integrating Databricks Jobs API for automated workflows.

• Built Snowflake-based pipelines using Stored Procedures, Streams, and Snowpark UDFs in Python for batch and real-time loads.

• Ensured high-performance query execution for real-time BI, optimizing SQL analytics for business insights.

• Enhanced data governance with Entra ID, implementing Azure AD for role-based access control in Azure Synapse.

• Optimized credit card transaction processing using Azure Cosmos DB with partition keys for low-latency transactions.

• Configured Azure Notification Hubs with Logic Apps for real-time alerts on transactions and fraud detection using ML models.

• Led enterprise-wide data security and governance with Azure Purview, developing metadata management frameworks and compliance policies.

• Led database migration (DBT) to Azure, defining governance policies with Azure Purview and ensuring security, compliance, and optimized performance.

• Developed and maintained end-to-end data pipelines for supply chain transactions, product catalogues, orders, and inventory management using Azure Databricks and ADF.

• Designed and implemented data solutions for pricing data, invoices, and receipts processing, integrating with ERP systems for real-time insights and operational efficiency — improved invoice processing time by 35%.

• Managed data integration for product identification codes and stock-keeping units (SKUs) across various internal and external systems, ensuring consistency and accurate reporting.

• Leveraged Azure Cosmos DB and Synapse for efficient storage and querying of supply chain transaction data, product catalogues, and order information across global business units.

• Enhanced order management and pricing data accuracy using Snowflake for efficient querying and high-performance analytics.

• Developed automated workflows for invoice processing and receipt tracking, ensuring compliance and streamlined operations using Azure Data Factory and Databricks.

• Integrated ERP systems with Azure Data Lake Storage for centralized, real-time reporting on supply chain, inventory, and order data.

• Ensured high data availability and scalability for product catalog management and stock tracking, using Databricks-powered analytics for optimized pricing and inventory strategies.

• Worked in Agile environments, participating in sprint planning, backlog refinement, daily stand-ups, and code reviews, leveraging JIRA and Git.

• Mentored junior engineers, conducting workshops on ETL design, performance tuning, and cloud-based data engineering solutions — increased team onboarding efficiency by 20%.

• Worked closely with teams to streamline integration between the Point of Sale (POS) system and inventory, ensuring seamless updates to stock levels and product catalogues.

Environment: Azure, Databricks, ADLS, Azure Synapse, Azure Stream Analytics, Azure Cosmos DB, Azure Data Factory (ADF), Power BI, Tableau, Snowflake, Databricks Delta Lake, Azure Cognitive Services, AKS, Python, PySpark, SQL, Java, Git, GitHub, MS fabric, Azure Event Hubs, Delta Live Tables, Azure Functions, Apache Airflow, Snowpark, Entra ID, Azure AD, Azure Key Vault, Databricks Secret Scopes, Azure Notification Hubs, Logic Apps, Azure Purview, DBT, ERP, JIRA.

Azure Data Engineer Jun 2017- Sep 2021

LogMeIn, Boston, MA

Responsibilities:

• Developed PySpark/Java, Python, and Spark SQL applications in Azure Databricks for data transformation and aggregation across Parquet, Avro, and Delta formats, improving model flexibility and processing.

• Managed High Availability clusters and Cluster pools on Azure Databricks to optimize large-scale data engineering jobs and improve team collaboration.

• Implemented Delta Lake in Azure Databricks for ACID transactions, schema evolution, and integrated with Azure Synapse Analytics for live dashboards in Power BI and Tableau.

• Created Azure Data Factory pipelines for large-scale ETL from various source systems like Oracle, SQL Server, and APIs.

• Analysed Snowflake data schemas and built dynamic models in PySpark/Java for adaptable data transformations.

• Monitored Azure Databricks jobs with Azure Monitor for performance tracking and proactive issue resolution.

• Developed Java-based publisher-consumer APIs for real-time data ingestion with Azure Event Hubs.

• Optimized streaming pipelines by parsing semi-structured data and using incremental loading techniques, improving performance and reducing costs.

• Collaborated with US and India teams for migrating corporate financial data from Azure Synapse Analytics, improving performance and data transition.

• Migrated on-premises data warehouse to Azure Synapse Analytics, reducing costs and enhancing scalability.

• Developed dynamic Data Models in Azure Synapse for BI integration, supporting OLAP and OLTP reports.

• Led data migration from on-premises to the cloud using command-line tools, improving transfer efficiency.

• Built pipelines for extracting insights from JSON payloads provided by third-party vendors, leveraging Azure Blob Storage and ADLS.

• Worked with DevOps team to develop app triggers for feeding data into ML models on Azure Kubernetes, enabling real-time inference.

• Designed scalable AI solutions on Azure, deploying using ARM templates and ensuring high availability.

• Utilized Azure DevOps CI/CD pipelines to create reproducible cloud environments, enhancing developer collaboration.

• Introduced Logic Apps and Function Apps for automated monitoring and alerting, ensuring timely data ingestion execution. Implemented Azure Functions for event-based and time-based scheduling, reducing manual intervention.

• Managed external and internal stages in Snowflake, using Snow pipe for seamless data ingestion and reducing latency.

• Optimized data manipulations with Snowpark in Snowflake, improving performance for analytics.

• Defined Hot and Cold storage/cache policies for cost-effective data retrieval and management using Azure Blob and ADLS.

• Applied Data Wrangling techniques using PySpark to clean and restructure datasets for high-quality processing. Utilized Zero Copy Cloning for efficient data versioning and replication across pipelines without extra storage costs.

• Implemented security strategies across Azure environments, using Azure Purview for metadata management and compliance.

• Enforced Data Governance policies, ensuring secure access and compliance across cloud environments.

• Led Agile teams for timely delivery of data solutions, incorporating stakeholder feedback in sprints.

• Managed Waterfall-based projects for large-scale data migrations, overseeing the entire project lifecycle to meet business goals.

Environment: Azure Databricks, PySpark, Java, Python, Spark SQL, Parquet, Avro, Delta Lake, Azure Synapse Analytics, Power BI, Tableau, Azure Data Factory, Snowflake, Azure Monitor, Azure Event Hubs, ADLS, Azure Blob Storage, Azure Kubernetes, ARM Templates, Azure DevOps, CI/CD, Logic Apps, Function Apps, Azure Functions, Snowpipe, Snowpark, Zero Copy Cloning, Azure Purview, Data Governance, Agile, Waterfall.

Big Data Engineer Sep 2014- Apr 2016

American Express, Mumbai, India

Responsibilities:

• Designed and built ELT/ETL pipelines for Finance analytics by integrating Salesforce, ServiceNow, and DataStage with AWS and Redshift, enabling secure data transfer and HDFS integration for analytics.

• Automated full data loads, incremental extracts, and end-to-end workflows, exporting data for Tableau and Workday Prism Analytics, and developed Salesforce Bulk API integration using Apache Sqoop and NiFi.

• Created Java-based scripts for automating Hadoop Name Node, Data Node, Spark worker, and master server operations, developed UDFs for business logic, and ingested log data into HDFS using Flume.

• Designed and implemented MapReduce jobs in Java for data cleansing, improving data quality, accuracy, and distributed processing performance.

• Developed custom Pig loaders, storage classes, and PIG Latin scripts to process JSON, XML, and web server output files, storing extracted data in HDFS.

• Optimized Hive query execution using Apache Tez and ORC file format, reducing disk access for Pig and Hive jobs and improving query retrieval speed.

• Applied distributed caching, indexing, partitioning, and vectorized queries in Hive, cutting processing costs by 35% through efficient execution.

• Implemented Avro and Parquet in Hive, converting Hive/HQL queries into Spark transformations using RDDs, Data Frames, Python, and Java, achieving a 40% reduction in execution time.

• Managed Apache Kafka-based data ingestion, developing Java producer-consumer APIs for seamless data transfer.

• Configured Zookeeper for cluster management, enabling concurrent Hive table access with shared and exclusive locking mechanisms, and optimized Apache Spark jobs with Java.

• Applied broadcast joins, partitioning, bucketing, and salting in Apache Spark with PySpark and Java, minimizing shuffling and enhancing large dataset processing.

• Improved Spark job efficiency with Predicate Pushdown Optimization and Zero Data Serialization using Apache Arrow for faster queries.

• Developed custom Spark SQL aggregate functions, created tables, performed interactive queries, and orchestrated ETL workflows.

• Developed complex DAX measures and calculated columns in Power BI to optimize financial reporting, sales forecasting, and customer segmentation analytics.

• Optimized Power BI performance using advanced DAX functions like CALCULATE, FILTER, and SUMX to enhance report responsiveness and data visualization efficiency.

• Conducted CRM data analysis and visualization with IBM DB2 and Tibco Spotfire, optimizing DataStage jobs using partitioning, node configuration, and Teradata scripts.

• Provided production on-call support, ensuring pipeline deployments, system stability, rapid issue resolution, and implemented SCD Type 2 for account history tracking.

• Integrated SWIFT (.mt, .txt, .xml) formats to process international transactions and financial data exchange, ensuring compliance with global standards for secure financial transactions.

• Developed cash management solutions, automating financial reporting and generating transaction reports for accurate invoice processing and timely financial statements.

• Implemented Open Financial Exchange (OFX) standards for seamless communication of financial transactions, optimizing data transfer across banking systems.

• Supported KYC/AML processes for customer and fraud prevention, integrating automated checks into financial systems to comply with regulatory requirements and improve security.

• Enforced PCI DSS standards in payment card data security, ensuring the protection of sensitive customer information across transactions and storage.

• Implemented ACH & Wire Transfer solutions to streamline payment processing, automating fund transfers and improving transaction accuracy.

• Utilized ISO 20022 financial messaging standards for seamless messaging between financial institutions, improving interoperability and data exchange efficiency.

Environment: Redshift, HDFS, Salesforce, ServiceNow, DataStage, Apache Sqoop, NiFi, Hadoop, Spare,Flume, MapReduce, Pig, Hive, Apache Tez, ORC, Avro, Parquet, RDDs, DataFrames, Kafka, Zookeeper, PySpark, Java, Apache Arrow, Spark SQL, IBM DB2, Tibco Spotfire, Teradata, SWIFT, OFX, KYC/AML, PCI DSS, ACH, Wire Transfer, ISO 20022.

Data Warehouse Engineer Nov 2012 - Aug 2014

Optum, Hyderabad, India

Responsibilities:

• Designed, planned, and implemented high-performance application domains, conducting functional analysis and knowledge transfers to document business activities for internal referencing.

• Gathered requirements from functional/end users for core reporting systems, successfully implementing ETL and reporting system business logic.

• Coordinated data conversion tasks, developed user interfaces, and facilitated system integration by extracting data from Oracle, Flat Files, and XML Files using SQL Loader and Freehand SQL.

• Conducted data analysis for source and target systems, applying Data Warehousing concepts like staging tables, Dimensions, Facts, Star Schema, and Snowflake Schema.

• Utilized Informatica PowerCenter for Update/Insert actions, supporting incremental data loads to maintain retail data warehouse consistency.

• Developed and scheduled DTS/SSIS packages, SQL Mail Agent, alerts, and automated processes for data management, extracting patient health records for Actuarial Datamart.

• Managed Erwin models for logical/physical data modelling of CDS, ADM, and Reference DB, and architected a data quality framework with Informatica MDM for data integrity and compliance.

• Executed projects using the Waterfall methodology, following structured sequential phases for project delivery.

• Designed and implemented a retail data warehouse/Datamart using Informatica PowerCenter, integrating OLTP system data into Fact and Dimension tables.

• Integrated data solutions using IICS, enabling seamless data movement across cloud environments for scalable and efficient processing.

• Optimized database performance by developing efficient T-SQL triggers, stored procedures, and functions, improving query execution time, and managing users, roles, and permissions in SQL Server with SSIS and Quality Centre for issue tracking.

Environment: Informatica PowerCenter, ETL, Data Warehousing, SQL Server, DTS/SSIS, Data Mart, OLTP, Star Schema, Snowflake Schema, Informatica MDM, Erwin Data Modeling, T-SQL, SQL Loader, Freehand SQL, Data Quality Framework, IICS, Oracle, Flat Files, XML Files, Patient Health Records, Data Conversion, Incremental Data Loads, Retail Data Warehouse, Waterfall Methodology, Actuarial Datamart, T-SQL Triggers, Stored Procedures, Functions, Quality Centre, Cloud Data Solutions, System Integration, Automated Data Management Processes, SSIS Packages, Data Governance, Dimensional Modeling, Data Integrity, Compliance, Functional Analysis, User Interface Development.

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