Global Business Intelligence Platform

I served as the Architect for Nissan's Global Business Intelligence Platform, a crucial initiative to provide comprehensive data insights and reporting capabilities for Global Financial Services. This platform was designed to empower business users with data-driven decision-making through interactive dashboards, advanced analytics, and self-service reporting. The solution leveraged a modern data stack comprising Tableau, Talend ETL, and Snowflake, deployed on the AWS cloud.

The Global Business Intelligence Platform was built on a robust and scalable architecture, incorporating the following key components:

1. Data Ingestion and Transformation (Talend ETL): Talend was used as the ETL (Extract, Transform, Load) tool to ingest data from diverse source systems across the globe. This involved:
   1. Extracting data from various structured and unstructured sources.
   2. Transforming and cleansing the data to ensure quality and consistency.
   3. Loading the transformed data into the Snowflake data warehouse.
2. Data Storage and Warehousing (Snowflake): Snowflake provided a cloud-based data warehouse for storing and managing the consolidated financial services data. Its key capabilities included:
   1. Scalable storage and compute resources to handle large volumes of data.
   2. Support for structured and semi-structured data.
   3. High performance for complex queries and analytics.
3. Data Visualization and Reporting (Tableau): Tableau was used as the primary data visualization and reporting tool, enabling:
   1. Creation of interactive dashboards and reports to visualize key performance indicators (KPIs) and business metrics.
   2. Self-service reporting capabilities, allowing business users to explore data and create their own reports.
   3. Data discovery and ad-hoc analysis to uncover hidden insights and trends.
4. Data Lake Integration: The platform integrated with an existing data lake (or if one didn't exist, I designed the integration strategy for a future data lake also in cases I created one) to provide access to raw, unprocessed data for advanced analytics, data science initiatives, and other downstream use cases. This included:
   1. Storing raw data in its native format within the data lake.
   2. Establishing data governance policies to manage access and security.
   3. Enabling data exploration and experimentation using tools like Spark and other data processing frameworks.
5. Data Governance and Security: Implementing robust data governance and security measures were paramount, including:
   1. Data access control and user authentication.
   2. Data lineage tracking to understand the origin and transformation of data.
   3. Data quality monitoring and validation to ensure data accuracy and reliability

As the architect for the Global Business Intelligence Platform, I was responsible for:

1. Architecture Design: Designing the overall architecture of the platform, including data flow, data modeling, ETL processes, data warehousing strategy, and data lake integration.
2. Technology Selection: Selecting the appropriate technologies (Tableau, Talend ETL, Snowflake, AWS services) based on project requirements, scalability needs, and budget constraints.
3. Data Modeling and Governance: Designing the data model for the data warehouse and defining data governance policies to ensure data quality, consistency, and security.
4. ETL Design and Development Oversight: Overseeing the design and development of the ETL processes in Talend, ensuring efficient data ingestion and transformation.
5. Infrastructure Design and Deployment (AWS): Designing the infrastructure on AWS to host the platform components, including network configuration, security settings, and scalability considerations.
6. Collaboration with Stakeholders: Collaborating with business stakeholders, IT teams, and other relevant parties to gather requirements, validate designs, and ensure alignment with business objectives.
7. Security and Compliance: Working with security and compliance teams to ensure the platform met all relevant security and regulatory requirements.
8. Oversight of Implementation: Providing technical guidance and oversight during the implementation phase to ensure the successful delivery of the platform.

Spring Boot

Intermediate

PostgreSQL

Intermediate

Elasticsearch

Intermediate

AWS

Intermediate

Tableau

Intermediate

Power BI

Intermediate

Talend

Intermediate

Apache Camel

Intermediate

Docker

Intermediate

Java

Intermediate

BitBucket

Intermediate

Bamboo

Intermediate

Kibana

Intermediate

Logstash

Intermediate

Snowflake

Intermediate