Deepak Mane

Prepared data design models and implemented a comprehensive data integration solution leveraging Google Cloud Platform and Dataflow for large-scale DWH migration.

Designed and executed a robust Data Governance strategy for migration projects, aligning diverse stakeholder needs (Business, Technology, Operations).

Implemented a target data storage architecture utilizing Google Storage and Google Databricks (Medallion – Bronze, Silver, Gold layers) to optimize data organization and accessibility.

Managed the complete migration of Oracle SQL queries to Databricks SQL format, achieving 100% data modeling accuracy and efficiency.

Established a secure integration runtime environment, including SIX FTS, to facilitate smooth data ingestion from core source systems.

Developed and deployed security and monitoring solutions using Google Monitor, ensuring data integrity and compliance across the cloud platform.

Converted complex Oracle SQL queries into Databricks SQL using LLM models, streamlining the migration process and enhancing data transformation capabilities.

Optimized and developed Google Data Fusion pipelines, reducing data ingestion time by 30% and significantly enhancing overall reporting efficiency.

Streamlined data flow by creating robust ETL pipelines in Google Data Fusion, decreasing data loading time by 25%.

Implemented a Google Data Lake Store for thin provisioning, resulting in a 20% reduction in data storage costs.

Designed and implemented a medallion architecture (Bronze, Silver, Gold layers) for processing and transforming data using Databricks and Google Storage.

Developed and deployed a Log Analytics framework using KQL and Google Data Fusion, enhancing operational monitoring and troubleshooting capabilities.

Designed a scalable data ingestion pipeline using Google Data Fusion, processing over 1 million records per hour from diverse sources.

Implemented comprehensive data governance and security measures, ensuring data quality, integrity, and compliance with regulatory requirements.

Collaborated with cross-functional teams to integrate the analytics platform with existing systems and applications, improving data accessibility.

Designed, built, and deployed Amazon Machine Images (AMI) and Elastic Container Registry (ECR) using automated pipelines and scripts, streamlining infrastructure provisioning.

Developed an automated cost optimizer model leveraging CloudWatch and AWS Trust Advisor, achieving significant cost savings for cloud resources.

Created an advanced error handling and log exception framework using AWS CloudWatch, monitoring, and audit logs to improve system reliability.

Developed and implemented scripts for automated patching at the OS, application, and code levels, ensuring system security and compliance.

Published efficient physical data models for NoSQL, SQL, and message-based target data layers, optimizing data storage and retrieval.

Resolved performance bottlenecks in data pipelines and transformation processes, improving system throughput and efficiency.

Integrated Spark with Abinitio and AWS Kinesis to enable real-time data streaming and processing.

Engineered and optimized Spark and Airflow for performance tuning, enhancing the efficiency of data processing workflows.

Designed, developed, and documented various machine learning models for world-class enterprise applications, ensuring high accuracy and scalability.

Led and collaborated with the Bank's Financial Security team to resolve critical issues, enhancing fraud detection capabilities.

Developed a machine intelligence-based model for auto-classification of transactions and entity extraction using NLTK, achieving high data accuracy.

Created advanced Graph-based machine learning models (e.g., community detection, similarity, heuristic pattern, path finding, search) for complex fraud patterns.

Achieved real-time processing of transactions in milliseconds, crucial for immediate fraud detection and prevention.

Developed a real-time data ingestion model using Kafka with CDC from various sources, ensuring continuous and up-to-date data for analysis.

Led cloud platform provisioning, build deployment, and release management, ensuring seamless deployment and operation of the fraud detection system.

Developed a logging and feedback framework to ensure data delivery to storage (AWS Kinesis) with high reliability.

Implemented a real-time data ingestion platform leveraging AWS Kinesis in the cloud for high-throughput data processing.

Managed cloud platforms (AWS and GCP) to ensure optimal performance, cost-efficiency, and operational stability.

Developed diverse machine learning models, including Collaborative Filtering, K-Means Clustering, and Bayesian Networking, to extract deeper customer insights.

Designed and developed a real-time online dashboard using Amazon Kinesis, ELK, and Python Pusher for immediate data visualization.

Developed a Data Quality/Transformation framework using Spark Scala, ensuring data integrity and consistency for analytics.

Designed a robust data ingestion architecture model based on 50 parameters, ensuring no zero data loss, high SAR, auditing, security, and compliance.

Applied advanced missing value treatments and outlier reduction techniques to improve data quality and model accuracy.

Developed a feature engineering extraction methodology using Python libraries to enhance predictive power from customer data.

Built a customer classification model using Light Machine Learning algorithms, achieving high accuracy in customer segmentation.

Developed an accurate sales prediction model using regression techniques, improving sales forecasting capabilities.

Created a time-series machine learning model to suggest optimal order quantities, optimizing inventory and supply chain.

Trained existing datasets to achieve high accuracy for machine learning models, ensuring reliable predictive outcomes.

Developed trend detection and co-relation finder to identify abnormal transaction behaviors, enhancing fraud pattern recognition.

Built a web crawler to gather intelligence on fraudulent cyber activity, expanding fraud detection capabilities.

Featured and modeled a fraud detection model using Spark, optimizing processing for large datasets.

Developed a classification model using four diverse algorithms (Boost, KNN, SVM, Random Forest) for robust fraud detection.

Increased the accuracy of existing machine learning models by 20% through the application of Boost algorithms.

Conducted detailed AS-IS analysis, gap analysis, and recommendations across three phases to inform the Enterprise Data Lake strategy.

Developed an end-to-end solution architecture model for the Enterprise Data Lake, integrating diverse fabrics (Integration, Knowledge, Delivery, Analytics, Information Management) across cloud (Amazon) and on-premises environments.

Designed a business use case onboarding model, incorporating Cost Benefit Analytics and Return on Investment metrics for new initiatives.

Implemented Change Data Capture using Kafka and Attunity for structured databases, ensuring real-time data synchronization.

Designed detailed Data Processing Flows for the data lake, including various layers and fabrics (Integration, Knowledge, Delivery, Analytics).

Developed a 12-month Data Ingestion Roadmap with integrated business use cases.

Provided a blueprint model for Hadoop Disaster Recovery, enhancing data resilience and business continuity.