Parikshit is available for hire
Hire ParikshitParikshit Agarwal
Verified Expert in Engineering
Data Architect and Developer
Jaipur, India
Toptal member since June 19, 2026
Bio
Parikshit is an enterprise data architect with 12+ years of experience designing cloud data platforms, data governance frameworks, and enterprise data models across finance, supply chain, banking, manufacturing, and telecom. He has expertise with data architecture, data quality, RBAC, metadata management, data lakes, and modern data engineering. He has a proven track record of leading large-scale transformation initiatives and delivering scalable, governed, AI-ready data solutions.
Portfolio
Exponentia.ai
Data Governance, Data Profiling, Security, DataZone, Data Mesh, ETL, Erwin...
DS Smith
Databricks, Amazon Web Services (AWS), Amazon S3 (AWS S3), Apache Spark...
ANZ
Google Cloud Platform (GCP), BigQuery, Pub/Sub, Apache Airflow...
Experience
- SQL - 15 years
- Data Governance - 12 years
- Amazon Web Services (AWS) - 12 years
- Google Cloud Platform (GCP) - 12 years
- Data Warehousing - 12 years
- Data Build Tool (dbt) - 12 years
- Data Modeling - 12 years
- Databricks - 8 years
Preferred Environment
Google Cloud Platform (GCP), Amazon Web Services (AWS), Databricks, SQL, Azure Data Factory (ADF), Data Build Tool (dbt), Snowflake, BigQuery, Python, Erwin, Data Architecture, Data Engineering
The most amazing...
...cloud-native enterprise data governance platform and canonical data models I've architected process 300+ TB of data across 200+ source systems.
Work Experience
Enterprise Data Architect
2024 - 2026
Exponentia.ai
- Performed data governance, comprising data profiling, auditing, compliance, security, and contracts. Defined sharing policies, data ownership, and stewardships.
- Architected enterprise data platforms for global manufacturing and supply chain clients.
- Designed canonical enterprise data models integrating 200+ source systems.
- Created an ETL framework supporting auditing, notification, delta loads, data quality checks, lineage, security, and availability.
- Performed data modelling using Erwin to define ER model and dimensional model.
- Worked across different project teams to serve and enrich the EDM model in AWS S3.
- Designed scalable cloud-native data lake and warehouse architectures on AWS.
- Implemented enterprise data cataloging and metadata management strategies.
- Led creation of AI-ready data foundations for analytics and machine learning initiatives.
- Defined RBAC and data security standards across multiple business domains.
Technologies: Data Governance, Data Profiling, Security, DataZone, Data Mesh, ETL, Erwin, Redshift, Data Modeling, Data Catalog Implementation, Role-based Access Control (RBAC), Data Quality, Data Architecture, Amazon Web Services (AWS), Amazon S3 (AWS S3), Data Build Tool (dbt), Apache Airflow, erwin Data Modeler, Python, SQL, Solution Architecture, Data Security, Master Data Management (MDM), Cloud, Data Engineering, Apache Spark, Azure, Claude Code, Enterprise Data Warehouse (EDW), Data Warehouse Design, Architecture, SQL Server, Database Architecture, Identity & Access Management (IAM), Cloud Storage, Compliance, Microsoft Power BI, Performance Optimization, CI/CD Pipelines, Dimensional Modeling, PostgreSQL, Data Lakes, Large Language Models (LLMs), AWS HA, Model Context Protocol (MCP), AWS Lambda, Amazon Athena, PySpark, Data Pipelines, Databricks, Data Analysis, Data Processing, Research, Data Analytics, Data Lake Design, Unity Catalog, Technical Consulting, Data Science, OpenAI, Artificial Intelligence (AI), Machine Learning
Enterprise Data Architect
2024 - 2025
DS Smith
- Built a scalable data engineering platform on Databricks using PySpark and Delta Lake for enterprise analytics.
- Developed ETL/ELT pipelines to ingest and transform data from multiple enterprise systems into curated datasets.
- Optimized Spark jobs and Delta Lake tables, improving pipeline performance, scalability, and reliability.
- Integrated Databricks with AWS services, including Amazon S3, to enable secure, cloud-native data processing.
- Implemented workflow orchestration and monitoring to improve batch processing reliability and operational efficiency.
Technologies: Databricks, Amazon Web Services (AWS), Amazon S3 (AWS S3), Apache Spark, PySpark, Delta Lake, ETL, SQL, Python, Performance Optimization, Workflow, Data Modeling, Cloud, Dimensional Modeling, PostgreSQL, Data Lakes, Large Language Models (LLMs), AWS HA, Model Context Protocol (MCP), AWS Lambda, Amazon Athena, Data Pipelines, Consumer Products, Data Analysis, Data Processing, Research, Data Analytics, Data Lake Design, Unity Catalog, Technical Consulting, Finance, Data Science, Artificial Intelligence (AI), Machine Learning
Data Architect
2024 - 2025
ANZ
- Architected a cloud-native Risk Data Hub processing 300+ TB of enterprise data on GCP.
- Designed Data Mesh-aligned governance, RBAC, metadata management, and data ownership frameworks.
- Designed enterprise data models supporting regulatory and risk analytics.
- Built scalable ingestion and transformation frameworks using BigQuery, Airflow, Pub/Sub, and dbt.
- Implemented metadata-driven orchestration and auditability frameworks.
- Standardized ingestion and transformation patterns using dbt and Airflow.
- Enabled self-service analytics through governed data products.
- Led architecture reviews and stakeholder alignment across business and technology teams.
Technologies: Google Cloud Platform (GCP), BigQuery, Pub/Sub, Apache Airflow, Data Build Tool (dbt), Python, SQL, Erwin, Data Vault 2.0, Data Mesh, Data Governance, Role-based Access Control (RBAC), Metadata, Solution Architecture, Data Security, Master Data Management (MDM), Cloud, Data Architecture, Data Engineering, Apache Spark, Enterprise Data Warehouse (EDW), Data Warehouse Design, Architecture, SQL Server, Snowflake, Database Architecture, ETL Pipelines, Google BigQuery, Google Cloud Storage, Google Cloud SQL, Identity & Access Management (IAM), Dataform, Cloud Storage, Compliance, Microsoft Power BI, Performance Optimization, CI/CD Pipelines, Dimensional Modeling, PostgreSQL, Data Lakes, Large Language Models (LLMs), AWS HA, AWS Lambda, Amazon Athena, PySpark, Data Pipelines, Databricks, Data Analysis, Data Processing, Research, Data Analytics, Data Lake Design, Technical Consulting, Finance, Artificial Intelligence (AI), Machine Learning, Microsoft Azure
Data Architect, Product Owner
2021 - 2023
CGI
- Led architecture modernization of enterprise finance and HR analytics platforms.
- Defined governance, security, cataloging, and enterprise reporting strategies.
- Successfully managed product roadmap and technical delivery for global stakeholders.
- Drove migration and modernization initiatives across BI platforms.
- Designed scalable reporting architectures supporting thousands of business users.
- Established enterprise data modeling standards and best practices.
- Improved stakeholder engagement through structured product ownership processes.
- Mentored technical teams and guided architectural decision-making.
- Delivered custom analytics solutions for strategic business requirements.
- Aligned technology investments with enterprise reporting and governance objectives.
Technologies: SQL, Oracle BI, Oracle Data Integrator (ODI), Oracle Business Intelligence Enterprise Edition 11g (OBIEE), Tableau, Oracle Database, Data Governance, Data Modeling, Oracle SQL Data Modeler, Product Ownership, Stakeholder Management, Amazon Web Services (AWS), Data Lineage, Data Security, Snowflake, Data Architecture, Data Engineering, Business Intelligence (BI), Apache Airflow, Enterprise Data Warehouse (EDW), Data Warehouse Design, Architecture, SQL Server, Big Data, Database Architecture, ETL Pipelines, ELT, Google BigQuery, Google Cloud Storage, Google Cloud SQL, Identity & Access Management (IAM), Cloud, Cloud Storage, Data Migration, Microsoft Power BI, Performance Optimization, Dimensional Modeling, PostgreSQL, Data Lakes, Python, Data Pipelines, Data Analysis, Data Processing, Research, Data Analytics, Technical Consulting, Presales, Finance, Microsoft Azure
Data Architect
2016 - 2021
BT Group
- Designed enterprise data warehouses, graph analytics solutions, and reporting platforms.
- Led data modeling, governance, and architecture initiatives for ITAM and customer analytics.
- Delivered advanced analytics solutions, including sentiment analysis and operational intelligence.
- Led IT asset management analytics transformation using relational and graph databases.
- Partnered with business stakeholders to define analytics strategies and roadmaps.
Technologies: Oracle, Neo4j, Tableau, Oracle Business Intelligence Enterprise Edition 11g (OBIEE), SQL, Data Warehousing, Solution Architecture, Graph Databases, Enterprise Architecture, Data Modeling, Oracle Data Integrator (ODI), Informatica, Data Lineage, Data Architecture, Data Engineering, Business Intelligence (BI), Data Warehouse Design, Architecture, SQL Server, Big Data, Database Architecture, ETL Pipelines, ELT, Google BigQuery, Cloud, Data Visualization, Dashboards, Data Migration, Performance Optimization, Dimensional Modeling, PostgreSQL, Python, Data Pipelines, eCommerce, Data Analysis, Data Processing, Research, Finance
BI and Data Engineering Consultant
2014 - 2016
TEKsystems
- Delivered enterprise data warehouse and analytics implementations for global clients.
- Developed ETL pipelines, dimensional models, and business intelligence solutions.
- Collaborated with business stakeholders to define reporting and analytics requirements.
Technologies: Oracle Data Integrator (ODI), Informatica, Oracle Business Intelligence Enterprise Edition 11g (OBIEE), Oracle Database, NoSQL, ETL, Data Warehousing, Data Modeling, Data Engineering, Business Intelligence (BI), Data Warehouse Design, SQL Server, Big Data, ETL Pipelines, ELT, Data Visualization, Dashboards, Data Migration, Dimensional Modeling, PostgreSQL, Data Pipelines, eCommerce, Data Analysis, Data Processing, Research, Technical Consulting, Presales, Technical Sales, Finance
Experience
Enterprise Risk Data Hub (ANZ Bank)
I architected a cloud-native Risk Data Hub for a leading global bank, enabling the governed ingestion, transformation, and consumption of over 300 TB of enterprise data. The platform consolidated both assured and unassured data sources into a scalable analytics ecosystem that supports risk management and regulatory reporting.
I led the design of enterprise data models, Data Mesh governance, RBAC, metadata management, and auditability frameworks. The solution leveraged event-driven ingestion, standardized transformation patterns, and self-service data products, significantly improving data discoverability, ownership, and operational efficiency. The platform established a trusted foundation for enterprise analytics and future AI initiatives.
I led the design of enterprise data models, Data Mesh governance, RBAC, metadata management, and auditability frameworks. The solution leveraged event-driven ingestion, standardized transformation patterns, and self-service data products, significantly improving data discoverability, ownership, and operational efficiency. The platform established a trusted foundation for enterprise analytics and future AI initiatives.
Enterprise Data Model and Data Governance Platform (DS Smith)
I led the design of an enterprise-wide data architecture initiative for a global manufacturing organization operating more than 200 ERP systems across multiple plants and mills. I developed a canonical enterprise data model that unified disparate operational systems into a governed and scalable data platform.
This project also involved establishing data governance processes covering ownership, stewardship, lineage, cataloging, security, and data quality. I designed the architecture for a cloud-native data lake and analytics platform that enabled consistent reporting, improved data trust, and laid the foundation for advanced analytics and AI-driven decision-making.
This project also involved establishing data governance processes covering ownership, stewardship, lineage, cataloging, security, and data quality. I designed the architecture for a cloud-native data lake and analytics platform that enabled consistent reporting, improved data trust, and laid the foundation for advanced analytics and AI-driven decision-making.
Metadata-driven Data Automation Framework
I designed and implemented a metadata-driven orchestration framework to automate large-scale data pipeline execution and governance. The platform supports event-based, schedule-based, and dependency-driven workflows through centralized metadata configuration rather than hard-coded orchestration logic.
Key capabilities include auditability, lineage tracking, SLA monitoring, data quality validation, automated notifications, and scalable execution across thousands of pipelines. The framework reduced operational complexity, improved maintainability, and accelerated onboarding of new data products while ensuring governance and compliance standards were consistently enforced.
Key capabilities include auditability, lineage tracking, SLA monitoring, data quality validation, automated notifications, and scalable execution across thousands of pipelines. The framework reduced operational complexity, improved maintainability, and accelerated onboarding of new data products while ensuring governance and compliance standards were consistently enforced.
Informatica-to-ODI Migration Automation Framework
https://info4j.com/informatica-to-odi-conversion-tool/I developed an automation framework to accelerate the migration from Informatica PowerCenter to Oracle Data Integrator (ODI). The solution analyzes Informatica metadata, mappings, workflows, and transformations, then automatically generates ODI-compatible artifacts and configurations.
The framework significantly reduces manual effort, migration risks, and project timelines while ensuring consistency across large migration programs. Built using Java and Python, the solution incorporates metadata extraction, rule-based conversion, validation mechanisms, and automated documentation generation.
The framework significantly reduces manual effort, migration risks, and project timelines while ensuring consistency across large migration programs. Built using Java and Python, the solution incorporates metadata extraction, rule-based conversion, validation mechanisms, and automated documentation generation.
RelGraph, Relational to Graph Synchronization
https://info4j.com/relgraph-sync-relational-graph-db/I created an automated framework for synchronizing bulk and incremental data from relational databases into graph databases. The solution eliminates the complexity of maintaining graph structures by automatically detecting and propagating data changes while preserving relationships and dependencies.
Designed for high-volume environments, the framework supports near real-time synchronization and enables organizations to leverage graph analytics without redesigning existing operational systems. The project demonstrates expertise in data integration, graph databases, automation, and scalable architecture design.
Designed for high-volume environments, the framework supports near real-time synchronization and enables organizations to leverage graph analytics without redesigning existing operational systems. The project demonstrates expertise in data integration, graph databases, automation, and scalable architecture design.
Enterprise IT Asset Management Analytics Platform (British Telecom)
I architected an enterprise analytics platform for IT asset management, integrating data from multiple operational systems into both relational and graph-based data stores. The platform provided end-to-end visibility into asset relationships, dependencies, ownership, and lifecycle management.
Leveraging graph analytics and enterprise reporting, the solution enabled improved decision-making, impact analysis, and operational efficiency. The architecture incorporated governance controls, data quality processes, and scalable reporting capabilities supporting a large telecommunications organization.
Leveraging graph analytics and enterprise reporting, the solution enabled improved decision-making, impact analysis, and operational efficiency. The architecture incorporated governance controls, data quality processes, and scalable reporting capabilities supporting a large telecommunications organization.
Education
2010 - 2014
Bachelor's Degree in Computer Science And Engineering
Suresh Gyan Vihar University - Jaipur, India
Certifications
JULY 2026 - PRESENT
Databricks Certified Data Engineer Professional
Databricks
Skills
Libraries/APIs
PySpark
Tools
Erwin, Oracle Business Intelligence Enterprise Edition 11g (OBIEE), BigQuery, Apache Airflow, Microsoft Power BI, Tableau, Claude Code, Amazon Athena, Qlik Sense, Jira, Git, Oracle SQL Data Modeler
Languages
Python, SQL, Snowflake, Java, XML, YAML
Paradigms
ETL, Business Intelligence (BI), Dimensional Modeling, Role-based Access Control (RBAC), Model Context Protocol (MCP), Event-driven Architecture, Incremental Development
Platforms
Google Cloud Platform (GCP), Amazon Web Services (AWS), Databricks, Oracle Data Integrator (ODI), Oracle, Azure, AWS Lambda, Oracle Database, Kubernetes, Dataform
Storage
Redshift, Amazon S3 (AWS S3), Database Architecture, Data Lakes, Data Lake Design, Master Data Management (MDM), Google Cloud Storage, Google Cloud SQL, PostgreSQL, Data Pipelines, MSSQLCE, MySQL, Database Management Systems (DBMS), Neo4j, Graph Databases, NoSQL, Data Synchronization
Frameworks
Apache Spark, AWS HA, Spring 6, Angular
Other
Data Governance, DataZone, Data Build Tool (dbt), Data Warehousing, Data Modeling, Data Quality, Data Architecture, Solution Architecture, Analytics, Data Engineering, Enterprise Data Warehouse (EDW), ELT, Data Analysis, Data Processing, Research, Data Analytics, Technical Consulting, Finance, Data Profiling, Data Lineage, ETL Pipelines, Data Security, Cloud, Data Warehouse Design, Architecture, SQL Server, Big Data, Google BigQuery, Identity & Access Management (IAM), Data Visualization, Cloud Storage, Compliance, Dashboards, Data Migration, Performance Optimization, CI/CD Pipelines, Large Language Models (LLMs), Unity Catalog, Presales, Data Science, OpenAI, Artificial Intelligence (AI), Machine Learning, Microsoft Azure, Auditing, Security, Data Mesh, Informatica, Azure Data Factory (ADF), Data Structures, Software Development Lifecycle (SDLC), IT Project Management, Data Catalog Implementation, erwin Data Modeler, Pub/Sub, Data Vault 2.0, Metadata, Oracle BI, Product Ownership, Stakeholder Management, Enterprise Architecture, Access Control, Data Stewardship, Ownership, Machine Learning Operations (MLOps), Delta Lake, Workflow, Consumer Products, eCommerce, Technical Sales
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