Pranab Kumar Pattnayak

Implemented end-to-end modern data platforms on Microsoft Fabric — from data ingestion to transformation, modeling, governance, and enabling analytics for business insights.
Designed and developed data pipelines in Microsoft Fabric for ingestion, transformation, and processing.
Ingest and harmonize data from SAP ECC/S4HANA/Azure SQL systems
Implemented and managed Lakehouse architecture for scalable data storage and analytics.
Loaded structured and semi-structured data from File System into a Lakehouse in One Lake, enabling centralized and scalable storage for analytics and reporting.
Used Medallion Architecture and shortcuts to Improves Data Quality
Developed notebooks in PySpark to process and transform and pass to next layer
Expertise in Building and optimizing semantic models to support Power BI reporting
Developed ETL/ELT processes using Dataflows, Notebooks (PySpark/SQL), and Azure Data Factory
Extensive experience to ensure data quality, governance, and security across pipelines.
Optimized performance of queries, transformations, and storage.
Collaborating with business and BI teams to define KPIs and enable self-service analytics.
Hands-on experience in Monitor and troubleshoot pipelines using Fabric monitoring tools and Azure services.

Implemented end-to-end modern data platforms on Microsoft Fabric — from data ingestion to transformation, modeling, governance, and enabling analytics for business insights., Designed and developed data pipelines in Microsoft Fabric for ingestion, transformation, and processing., Ingest and harmonize data from SAP ECC/S4HANA/Azure SQL systems, Implemented and managed Lakehouse architecture for scalable data storage and analytics., Loaded structured and semi-structured data from File System into a Lakehouse in One Lake, enabling centralized and scalable storage for analytics and reporting., Used Medallion Architecture and shortcuts to Improves Data Quality, Developed notebooks in PySpark to process and transform and pass to next layer, Expertise in Building and optimizing semantic models to support Power BI reporting, Developed ETL/ELT processes using Dataflows, Notebooks (PySpark/SQL), and Azure Data Factory, Extensive experience to ensure data quality, governance, and security across pipelines., Optimized performance of queries, transformations, and storage., Collaborating with business and BI teams to define KPIs and enable self-service analytics., Hands-on experience in Monitor and troubleshoot pipelines using Fabric monitoring tools and Azure services.

Technical Project Manager specializing in designing, developing, and optimizing data pipelines, ETL processes, and database solutions. Proficient in Azure Fabric, Azure Data Factory, Azure data bricks, PySpark, Azure Synapse, SSIS, SQL development, Power BI and, and data migration with expertise in ensuring seamless data integration across multiple systems. Adept at transforming raw data into structured formats, ensuring high data quality and enabling business insights.Leading a team of 10 data engineers, ensuring project deadlines are met while providing hands‑on technical guidance and support as needed.A keen ability to execute and deliver results, including delivering technical projects on time and on budget through planning, execution, and delivery.Understanding product portfolios, aligning business functions and operational processes, and ensuring teams deliver stakeholder value across methodologies (Agile, Waterfall, etc.).Quick thinker with the agility to re‑prioritize multiple projects and tasks without compromising quality, consistently driving outcomes with a strong sense of urgencyEnsures the software development lifecycle (SDLC) is followed consistently & effectively through CI/CD, tooling, and collaboration with stakeholders.Work closely with senior team members and stakeholders to gather and clarify basic business requirements and ensure they are accurately documented and understood.Experienced in conducting Software Management Reviews (SMR) and leading audit processes aligned with CMMI Level 5 standards, ensuring compliance, process maturity, and continuous improvement across projects.

Developed end-to-end pipelines with Azure Data Factory, Azure Databricks, and Synapse Analytics, enabling seamless ETL/ELT processes for enterprise-grade data ingestion and transformation using metadata driven architecture.
Implemented Azure key vault, parametrized link service and dataset, scheduled trigger and Logic Apps
Implemented Unity Catalog for access control and lineage tracking across Databricks workspaces.
Collaborated on Extract, Transform, Load (ETL) workflows, ensuring data accuracy and stability across ingestion and transformation pipelines from various sources like SQL, Adls Gen2, Webcon API, Share Point, Oracle
Architected scalable Azure storage solutions (Azure SQL Database, Data Lake Storage), balancing performance and cost-efficiency for large-scale data operations.
Designed and implemented Delta Lake architecture to support ACID transactions, time travel, and schema evolution for reliable data lake operations.
Created dimensional data models in Databricks to support analytical workloads and BI dashboards.
Established robust data validation, testing, and monitoring frameworks to ensure pipeline reliability and preserve data integrity.
Optimized Spark jobs in Databricks by tuning cluster configurations, caching, and partitioning strategies, resulting in 25% faster job execution times.
Developed Databricks notebook in python and spark to process and transform different source and create parquet in ADLS location
Integrated multiple data sources including SQL, Webcon, SharePoint, SAP, JSON, ADLS Gen2 and Excel into data pipelines.
Collaborated with stakeholders to ensure business requirements were met through optimized data integration.
Used Z-ordering and file compaction techniques in Delta tables to improve query performance on frequently accessed datasets.
Collaborated with cross-functional teams, including data scientists, analysts, and developers, to deliver well-curated datasets for advanced analytics, machine learning, and reporting purposes.
Enforced data security and compliance with regulations (e.g., GDPR, CCPA) by integrating robust measures like Azure Security Center, RBAC (Role-Based Access Control), and encryption protocols.

Developed end-to-end pipelines with Azure Data Factory, Azure Databricks, and Synapse Analytics, enabling seamless ETL/ELT processes for enterprise-grade data ingestion and transformation using metadata driven architecture., Implemented Azure key vault, parametrized link service and dataset, scheduled trigger and Logic Apps, Implemented Unity Catalog for access control and lineage tracking across Databricks workspaces., Collaborated on Extract, Transform, Load (ETL) workflows, ensuring data accuracy and stability across ingestion and transformation pipelines from various sources like SQL, Adls Gen2, Webcon API, Share Point, Oracle, Architected scalable Azure storage solutions (Azure SQL Database, Data Lake Storage), balancing performance and cost-efficiency for large-scale data operations., Designed and implemented Delta Lake architecture to support ACID transactions, time travel, and schema evolution for reliable data lake operations., Created dimensional data models in Databricks to support analytical workloads and BI dashboards., Established robust data validation, testing, and monitoring frameworks to ensure pipeline reliability and preserve data integrity., Optimized Spark jobs in Databricks by tuning cluster configurations, caching, and partitioning strategies, resulting in 25% faster job execution times., Developed Databricks notebook in python and spark to process and transform different source and create parquet in ADLS location, Integrated multiple data sources including SQL, Webcon, SharePoint, SAP, JSON, ADLS Gen2 and Excel into data pipelines., Collaborated with stakeholders to ensure business requirements were met through optimized data integration., Used Z-ordering and file compaction techniques in Delta tables to improve query performance on frequently accessed datasets., Collaborated with cross-functional teams, including data scientists, analysts, and developers, to deliver well-curated datasets for advanced analytics, machine learning, and reporting purposes., Enforced data security and compliance with regulations (e.g., GDPR, CCPA) by integrating robust measures like Azure Security Center, RBAC (Role-Based Access Control), and encryption protocols.

Designed and developed ETL workflows using Azure Data Factory for enterprise data processing.
Implemented Databricks Autoloader for scalable, incremental ingestion of streaming and batch data into Delta Lake
Architected Delta Sharing workflows to provide real-time, read-only access to curated Delta tables, improving data transparency and reducing duplication across teams
Designed and developed ETL workflows using Databricks/Spark for enterprise data processing.
Migrated large-scale SQL databases from SQL Server 2012 to Azure SQL 2019.
Developed JSON-to-tabular transformation logic to extract and load data into SQL Server.
Implemented data validation, error handling, and alerting mechanisms in ETL processes.
Built advanced Mapping Data Flows with transformations such as Filter, Aggregate, Join, Derived Column, Conditional Split, and Sink to deliver clean, curated datasets.

Designed and developed ETL workflows using Azure Data Factory for enterprise data processing., Implemented Databricks Autoloader for scalable, incremental ingestion of streaming and batch data into Delta Lake, Architected Delta Sharing workflows to provide real-time, read-only access to curated Delta tables, improving data transparency and reducing duplication across teams, Designed and developed ETL workflows using Databricks/Spark for enterprise data processing., Migrated large-scale SQL databases from SQL Server 2012 to Azure SQL 2019., Developed JSON-to-tabular transformation logic to extract and load data into SQL Server., Implemented data validation, error handling, and alerting mechanisms in ETL processes., Built advanced Mapping Data Flows with transformations such as Filter, Aggregate, Join, Derived Column, Conditional Split, and Sink to deliver clean, curated datasets.

Designed and managed ETL processes to load data from flat files, XML, and Excel, Oracle into SQL Server., Implemented error handling, logging, and alerting to improve data pipeline reliability., Developed and maintained database objects such as tables, views, stored procedures, and triggers., Conducted performance tuning using SQL Profiler and Database Engine Tuning Advisor.

Send email for maturity and new registration by using scripting language, Created and maintained SQL databases to support business operations, Developed ETL processes for data extraction, transformation, and loading., Involved data migration, access control, data governance, audit and conducted validation to ensure data consistency.

Implementing data solutions using Azure Data Factory and advanced data modeling techniques. Proficient in optimizing data pipelines and ensuring data quality for informed business decisions., Integrated Get Metadata, Lookup, and Set Variable activities to build dynamic, metadata‑driven pipelines that adapt to changing schema and file structures., Implemented robust error handling and retry policies using conditional activities (If Condition, Switch, Until) to ensure resilient data workflows, Secured pipelines with Managed Identity and Azure Key Vault, ensuring compliance and safe credential management for enterprise data integration, Migrated PeopleSoft data to Workday systems, ensuring high data integrity and minimal downtime., Optimized stored procedures, triggers, and indexing to enhance system performance for existing SSIS ETL’s, Created and optimized data pipelines to transfer Workday data to SQL Server.