Summary:

• I have dynamic 15+ years’ experience as a Data Engineer/Agentic AI with a master’s in business Intelligence and deep experience analyzing commercial data using reporting tools like Tableau, Python, SAS, and SQL across Fintech companies.
• Well-versed in deriving viable solutions to complex business problems through big data analysis and management.
• Solid understanding of Power BI desktop, Power Query, and DAX formulas.
• Proficient in containerization and orchestration using Docker and Kubernetes for scalable deployment of ML applications.
• Implemented comprehensive monitoring and logging using Prometheus, Grafana, ELK Stack, and Azure Monitor to ensure model and infrastructure health.
• Designed and implemented MLOps solutions on AWS, GCP, and Azure, leveraging cloud-native services for scalable and resilient deployments.
• Utilized tools such as Apache Airflow, Luigi, and Kubeflow Pipelines to automate end-to-end ML workflows, from data ingestion to model deployment.
• Worked on hardware-software integration testing for satellite receiver systems and embedded devices
• Strong experience building business-facing data solutions using Snowflake, Tableau, and DBT, enabling analytics, reporting, and executive decision-making. Proven ability to translate complex data into actionable insights through scalable dashboards and data visualization platforms.
• Developed comprehensive monitoring and alerting systems using tools like Prometheus, Grafana, and CloudWatch to ensure high availability and performance.
• Implemented automated model drift detection systems to monitor and alert model performance degradation over time.
• Collaborative work with cross-functional teams and utilization of diverse technologies (Python, Scala, TensorFlow, PyTorch).
• Utilized python’s flask framework for building REST APIs on top of Data Lake (BigQuery, Cloud SQL).
• Achieved Continuous Integration &Continuous Deployment (CI/CD) for applications using Git, Azure Devops.
• Experience with Test driven development (TDD), Agile methodologies and SCRUM processes.
• Hands on solving problems which brings significant business value by building predictive & forcasting models utilizing structured & unstructured data.
• Extensive experience managing and engaging stakeholders at all levels including communicating technical concepts to non-technical audiences.

Areas of Expertise include:

Data Analysis | Data Science | SQL | Tableau | Data Visualization | Snowflake | Python | Stakeholder Engagement Agile Scrum Master | Agile Product Owner | Lean | CI/CD | TDD | Gap Analysis | Process Mapping Data Modelling | Process Improvement | Problem-solving | AI | GCP | Communication & Influencing

Data Visualization & BI:

Tableau, ThoughtSpot (or similar BI tools), Sigma Computing, Power BI

Collaboration & PM Tools:

Slack, Miro, Keynote, Quip, Jira, Confluence

Analytics & ML Platforms:

DataRobot (familiarity), Feature Engineering, Predictive Analytics

Big Data Technologies: Scala, Apache Spark, Hive SQL, Hadoop, Apache Kafka

EDUCATION

• Master’s. Information Technology Management | University of Texas, Dallas, USA 2015
• B.Tech. Information Technology | Narsee Monjee Institute of Management Studies, Mumbai, India 2010

PROFESSIONAL EXPERIENCE: –

Intuit

Sr Data Engineer/ AI Engineer                                                                                                         Aug 23 – Present

• I worked on multiple analytics projects like cohort analysis, GNS analysis, and mobile analysis for the QBLive team.
• Maintain and update the Intuit Assist (IAQB) Tableau dashboard.
• I ran the OIPRO A/B test experiment from scratch and built a Tableau dashboard to view the performance and present insights in the WinRoom Experiment review.
• Developed automated workflows for model retraining and deployment based on data drift and performance metrics using Apache Airflow and Kubeflow Pipelines.
• Integrated LLM-powered chatbots into eBay's messaging system to provide real-time responses to customer queries, improving response times and user satisfaction.
• Designed and implemented robust machine learning pipelines using Kubernetes(K8)/AKS with Argo Workflow orchestration, ensuring scalable and efficient end-to-end ML processes.
• Designed and implemented data pipelines in Snowflake to optimize data storage and retrieval for large-scale analytics projects.
• Developed Airflow DAGs for orchestrating ETL jobs across AWS Glue and EMR Spark clusters.
• Migrated legacy ETL jobs (Informatica/Talend) to AWS Glue & Lambda functions, reducing infra costs by 25%.
• Designed and optimized data pipelines using Snowflake and dbt, integrated with Sigma Computing for real-time reporting.
• Built and maintained Tableau dashboards supporting business teams for analytics, reporting, and decision-making
• Designed and implemented OCR pipelines for extracting structured data from PDFs, invoices, and scanned documents
• Delivered insights through data visualization to product, marketing, and finance stakeholders
• Collaborated with cross-functional teams using tools like Slack, Miro, and Jira for project delivery
• Translated business requirements into scalable data models and dashboards in Snowflake and Tableau
• Built batch data pipelines using Java and Spring Batch.
• Developed REST APIs for data access and integration with downstream systems.
• Experienced Data Scientist with expertise in Azure AI, machine learning, and big data analytics.
• Leveraged Microsoft Copilot and AMP (AI/ML Platform) to accelerate development of AI pipelines, automate code generation, and improve productivity in building LLM-based and agentic AI systems.
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• Designed and optimized prompts for AI language models, specifically leveraging OpenAI GPT and similar large language models (LLMs) for a variety of applications, such as content generation, automated summarization, and customer support automation.
• Designed and maintained business dashboards in Sigma Computing and Tableau.
• Led migration of legacy ETL processes to Snowflake and SnapLogic, improving performance by 60% and reducing ETL failures.
• Created reusable SnapLogic pipelines for incremental load from Salesforce, Workday, and on-prem SQL sources into Snowflake.
• Integrated Spotfire with enterprise data sources (Snowflake, BigQuery, Oracle, APIs)
• Designed scalable SnapLogic pipelines to ingest data from REST APIs, SAP, and S3 into Snowflake with built-in error handling and alerting.
• Designed and maintained scalable ETL pipelines using Matillion to extract data from diverse sources (APIs, flat files, RDS) and load into Snowflake.
• Developed ELT jobs in Matillion, including use of Transformation and Orchestration components, scheduling jobs using built-in triggers.
• Designed and developed end-to-end data platform solutions on Azure using ADF, SHIR, Logic Apps, ADLS Gen2, and Snowflake.
• Reviewed and analyzed legacy on-premises ETL processes (SSIS, T-SQL) and migrated them to ADF pipelines for improved scalability and performance.
• Performed data validation testing to ensure consistency between on-prem SQL Server and cloud (Azure/Snowflake) environments.
• Built Python scripts to automate the generation of prompts for dynamic model interactions, enabling efficient workflows and reducing manual effort in prompt creation.
• Developed complex Informatica mappings and transformations for enterprise data integration.
• Optimized SQL queries and stored procedures for data retrieval and transformation.
• Integrated Informatica Cloud (IICS) for cloud-based ETL solutions, enhancing data pipeline efficiency.
• Managed data pipelines for ML workflows on Kubernetes(K8)/AKS using Argo Workflows, ensuring efficient data movement and transformation between pipeline stages.
• Built and maintained end-to-end machine learning pipelines, from data ingestion to model deployment, using tools like Apache Airflow, MLflow, and Kubeflow.
• Spearheaded the development and deployment of Generative AI models for content generation, creating text and image synthesis solutions that boosted product engagement by X%
• Implemented Variational Autoencoders (VAEs) and GANs for generating synthetic data, significantly improving the training data available for scarce classes.
• Built SageMaker Pipelines for automated training, testing, and deployment with integration into CI/CD workflows using CodePipeline and Lambda.
• Engineered deep learning models for NLP tasks, leveraging transformer architectures such as BERT, RoBERTa, T5, and GPT-based models.
• Built reusable Python ETL modules integrated with GCP services for modular pipeline development.
• Migrated on-prem ETL workflows to GCP, leveraging BigQuery and Cloud Storage.
• Ensured security and access control using GCP IAM roles and service accounts.
• Monitored pipelines using Cloud Logging and Monitoring and resolved production issues.
• Developed PySpark jobs on Dataproc for large-scale data transformation.
• Orchestrated workflows using Cloud Composer (Apache Airflow).
• Migrated legacy ETL jobs from on-premise Hadoop to GCP-based pipelines using Dataflow and BigQuery.
• Collaborated with cross-functional teams to develop NLP-based systems for text generation, sentiment analysis, and document classification, improving business process automation.
• Built cloud-based machine learning services for model serving, training, and batch inference using AWS Lambda and Google AI Platform.
• Integrated AI models into production systems for real-time data analysis and automated decision-making, resulting in improved operational efficiency.
• Utilized cloud-based ML services (AWS, GCP) to scale model training and deployment, significantly reducing processing time and cost.
• Collaborated with UX teams to deploy LLM-powered tools on internal dashboards via Streamlit.
• Designed, developed, and maintained data pipelines on Google Cloud Platform (GCP) using BigQuery, Dataflow, and Cloud Composer to ingest, process, and store data from multiple sources.
• Migrated on-premise data infrastructure to Google Cloud Platform, utilizing services like BigQuery, Cloud Storage, and Dataflow for seamless cloud integration.
• Leveraged Google BigQuery for creating data warehousing solutions and writing complex SQL queries for data extraction, aggregation, and transformation.
• Lead the design and development of simulation models to address supply chain challenges, such as inventory management, demand forecasting, and distribution network optimization.
• Developed and maintained data models that integrated historical data and real-time data from various sources to create accurate supply chain simulations and forecasts.
• Managed data warehouses and ensured that data quality and governance protocols were followed to maintain data integrity and security.
• Designed and maintained data warehouses using AWS Redshift and Google BigQuery, improving data access and retrieval times.
• Build and deploy predictive models using machine learning algorithms to forecast demand, optimize inventory levels, and reduce lead times.
• Designed and implemented CI/CD pipelines for machine learning models using Jenkins, GitLab CI, and Azure DevOps, ensuring rapid and reliable model deployment.
• Containerized machine learning applications using Docker and deployed them on Kubernetes clusters, ensuring high availability and scalability.
• Analyzed large datasets to identify key trends, insights, and opportunities for process improvement within the supply chain.
• Developed scalable data pipelines using Scala and Spark to transform raw web clickstream data into analytics-ready datasets.
• Implemented monitoring and logging solutions using Prometheus, Grafana, ELK Stack, and Azure Monitor to track model performance and infrastructure health.
• Utilized MLflow and DVC for model versioning, tracking, and reproducibility, ensuring consistent and reliable model deployments.
• Lead the development and deployment of large-scale NLP solutions using LLMs (ChatGPT, GPT-3.5, Claude, Mistral) to address business-critical problems, such as automated customer support, content generation, and sentiment analysis.
• Designed and implemented fine-tuning strategies for LLMs to create domain-specific models for applications in finance, healthcare, and e-commerce.
• Developed Spark code using Scala and Spark-SQL for faster processing and testing, integrating MLOps practices for efficient development workflows.
• Led migration of legacy Hadoop workflows to modern Databricks Lakehouse architecture, reducing job latency by 40%.
• Performed data cleaning and feature selection using MLlib package in PySpark, working with deep learning frameworks such as Caffe with considerations for MLOps.
• Integrated CI/CD pipelines with Argo Workflows and AKS to automate the deployment of updated machine learning models, ensuring continuous delivery and integration.
• Created an algorithm that can predict the type of the object in a typical house using Deep Learning. Used OpenCV for the image analysis and keras and Tensorflow for implementing artificial neural networks (ANN).
• Developed doctor report cards for real-time insights into their performance over the years. Using Apache Kafka for data ingestion and Tableau integrated with Hadoop/Spark for creating the reports.
• Create a self-serve tool to view DIWM trailers performance and automate all the processes.
• Make a lot of enhancements to the GSU SOT dashboard.

Apple (Sunnyvale)                                                                                                         Jun 21 – Aug 23

Senior Data scientist

• Implement BERT NER NLP model on Apple Care raw unstructured data to identify personal health information of customers and redact it.
• Create API also to find PHI and customer passwords from raw customer data.

Achievements/Tasks

• Collaborated with data engineers and operation team to implement ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements.
• Built NLP models including BERT, and XGBoost to find personal health information.
• Performed univariate and multivariate analysis on the data to identify any underlying pattern in the data and associations between the variables.
• Designed and developed Power BI dashboards integrated with Palantir Foundry, enabling real-time analytics for supply chain and operational performance.
• Designed and implemented Foundry pipelines to ingest structured and semi-structured data from multiple source systems.
• Architected and implemented Palantir Foundry pipelines for large-scale enterprise datasets.
• Built Foundry data pipelines to transform and ingest data from multiple sources into Power BI datasets, optimizing ETL workflows.
• Worked with Google Dataproc and Apache Spark to handle batch processing tasks for large-scale data analysis.
• Led the migration of on-premise data lakes to Google Cloud Storage and integrated BigQuery for real-time querying.
• Designed end-to-end RAG pipelines using LangChain + FAISS + OpenAI GPT for real-time question answering.
• Developed data-driven dashboards and analytics solutions to support business and operational teams
• Presented insights using visualization tools and storytelling techniques to stakeholders and leadership
• Automated ETL jobs with Cloud Composer (Airflow), ensuring daily, scheduled, and event-triggered data transformations ran efficiently.
• Performed data imputation using Scikit-learn package in Python.
• Analyzed customer data and market trends to identify new growth opportunities.
• Utilized data visualization tools and statistical analysis to create comprehensive reports and presentations, providing valuable insights to the executive team and supporting data-driven decision-making.
• Participated in features engineering’s such as feature intersection generating, feature normalization and label encoding with Scikit-learn pre-processing.
• Used Python 3.X (NumPy, SciPy, pandas, scikit-learn, seaborn) to develop various models and algorithms for analytic purposes.
• Created and managing reports, dashboards, and visualizations using Tableau.
• Built multiple Splunk dashboards for API usage and set up usage notifications on slack and email using slack webhooks.

GSK (India)                                                                                                         Oct 20 – Jun 21

Data Analytics Manager

• Owned and manage algorithm team at GSK. Implement Next Best Action project to 30 countries to drive pharma growth.

Achievements/Tasks

• Optimize the current algorithm use case to provide better recommendation to achieve 1.5% growth in revenue overall.
• Implement NBA project to 30 new global markets.