Mobeen Mehdi

Data Platform Engineer | Target Australia : Designed and developed a
scalable data platform, improving data processing speed by 35% and reducing
storage costs by 25%.
Integrated data from over 20 internal and external sources, enhancing data
quality and consistency across the organization.
Implemented automated CI/CD pipelines, reducing deployment time by 50%
and increasing platform reliability.
Optimized data pipelines, resulting in a 40% increase in data throughput and a
20% reduction in latency.
Enhanced inventory management and demand forecasting through real-time
data analytics, leading to a 15% reduction in stockouts and a 10% increase in
sale

DIGI Cell Malaysia : Designed and implemented scalable data streaming
pipelines with AWS Kinesis Firehose reducing data processing times by 40%
and enabling real-time analytics.
Utilized Kinesis Firehose to deliver streaming data to S3, Redshift, and
Elasticsearch, improving data accessibility and reducing data retrieval times by
30%.
Optimized migration processes and streaming pipelines, improving client
workload efficiency by 50% and enhancing overall system performance.
Collaborated with cross-functional teams to meet data requirements and
provided comprehensive post-migration support and documentation.

Designed and developed a scalable data platform, improving data processing speed by 35% and reducing storage costs by 25%.
Integrated data from over 20 internal and external sources, enhancing data quality and consistency across the organization.
Implemented automated CI/CD pipelines, reducing deployment time by 50% and increasing platform reliability.
Optimized data pipelines, resulting in a 40% increase in data throughput and a 20% reduction in latency.
Enhanced inventory management and demand forecasting through real-time data analytics, leading to a 15% reduction in stockouts and a 10% increase in sales.

Data Engineer | Essilor luxottica : Data Migration: Utilize Azure Data
Factory to extract and migrate data from on-prem Postgres to Azure Data Lake,
improving migration efficiency by 40%.
Data Lake Architecture: Implement a three-tier architecture (L1, L2, L3) in
the data lake to enhance data retrieval efficiency by 30%.
Data Integration: Integrate various data sources into a unified format using
ADF and Databricks, reducing data silos by 50%.
Data Transformation: Apply data integrity and transformation using ADF
Mapping Dataflows/Databricks, increasing data accuracy by 25%.
Error Handling: Develop Logic Apps for robust error handling in ADF data
pipelines.
Incremental Load & ARM Templates: Implement incremental load and
snapshot data ingestion strategies, and develop ARM templates for consistent
deployment across environments.
SAP BO Integration: Build SAP BusinessObjects (BO) views from
Databricks views, enabling semantic layer data access and KPI development.

Data Engineer
Data Engineer - Borealis : Developed Metadata-Driven Ingestion Framework
Automated data ingestion from JDBC and file sources, reducing manual effort
by 40% using Apache Airflow and Azure Big Data services.
Customized ingestion pipelines using dynamic dags for flexibility and
scalability, optimizing data workflows.
Managed seamless migration, minimizing downtime by 50% during transition.
Implemented efficient data integration pipelines, enhancing storage efficiency
by 25% using Mapping Dataflow.
Created templates for consistent pipeline deployment across environments.
Developed DBT code for dynamic snapshot-based transformations, ensuring
data reliability and reporting accuracy.

Unity Catalog POC - Johnson & Johnson : Deployed AWS Databricks
Lakehouse orchestrated deployment using Cloud Formation on AWS achieving
a 30% reduction in deployment time and costs.
Successfully conducted a POC demonstrating a 40% improvement in data
lineage clarity and governance efficiency.
Developed robust ETL pipelines resulting in a 25% increase in data processing
speed and accuracy within Databricks environment.
Implemented effective data governance strategies using Databricks Unity
Catalog, enhancing data integrity and compliance by 35%.
Centrally managed and governed all data assets across different Databricks
workspaces, ensuring 50% faster access and utilization

Data Pipeline - IOS Developer May 2019 - Jan2020
Responsibilities: Client - Valet Tracker
Worked on the Valet Tracker app, which is basically used for car parking and
helps drivers find parking easily
Handled 3 phases of this app, "Client Side", "Driver Side" and "Admin Side"

Designed and implemented scalable data streaming pipelines with AWS Kinesis Firehose reducing data processing times by 40% and enabling real-time analytics.
Utilized Kinesis Firehose to deliver streaming data to S3, Redshift, and Elasticsearch, improving data accessibility and reducing data retrieval times by 30%.
Optimized migration processes and streaming pipelines, improving client workload efficiency by 50% and enhancing overall system performance.
Collaborated with cross-functional teams to meet data requirements and provided comprehensive post-migration support and documentation.

Utilize Azure Data Factory to extract and migrate data from on-prem Postgres to Azure Data Lake, improving migration efficiency by 40%.
Implement a three-tier architecture (L1, L2, L3) in the data lake to enhance data retrieval efficiency by 30%.
Integrate various data sources into a unified format using ADF and Databricks, reducing data silos by 50%.
Apply data integrity and transformation using ADF Mapping Dataflows/Databricks, increasing data accuracy by 25%.
Develop Logic Apps for robust error handling in ADF data pipelines.
Implement incremental load and snapshot data ingestion strategies, and develop ARM templates for consistent deployment across environments.
Build SAP BusinessObjects (BO) views from Databricks views, enabling semantic layer data access and KPI development.

Deployed AWS Databricks Lakehouse orchestrated deployment using Cloud Formation on AWS achieving a 30% reduction in deployment time and costs.
Successfully conducted a POC demonstrating a 40% improvement in data lineage clarity and governance efficiency.
Developed robust ETL pipelines resulting in a 25% increase in data processing speed and accuracy within Databricks environment.
Implemented effective data governance strategies using Databricks Unity Catalog, enhancing data integrity and compliance by 35%.
Centrally managed and governed all data assets across different Databricks workspaces, ensuring 50% faster access and utilization.