Sep 2022 – Apr 2025 · 2 yrs 7 mos · Toronto, Ontario, Canada · Hybrid

• Responsibilities owned in the project:
• Collaborate with stakeholders from product, sales and marketing team to gather business and technical requirements for data solutions.
• Produced BRDs, FRDs, and SRS in collaboration with product and engineering teams to scope out end-to-end data flows and transformations.
• Utilized SQL and Python for data preparation, extraction, and analysis.
• Utilized Azure Data Factory, Azure Databricks for ETL orchestration and transformations using Pyspark.
• Introduced machine learning techniques to optimize workflows and tackle business challenges.
• Developed and deployed machine learning algorithms for product analysis and enhancement.
• Automated data analysis using ML models to drive informed decisions and improve product features.
• Created and maintained metrics for product performance tracking and strategic decision-making.
• Designed and deployed Alteryx workflows integrated with AWS Redshift, enabling near real-time data availability in the cloud warehouse.
• Leveraged Power Automate to schedule and trigger ETL workflows, automate report distribution, and streamline cross-platform data updates.
• Implemented user analytics using Heap, resulting in cost reduction and effective feature development.
• Supported data capture from SQL databases and conducted marketing data analysis.
• Collaborated with cross-functional teams throughout the product lifecycle.
• Identified process improvements, enhancing efficiencies and analytics capabilities.
• Coordinated with teams to optimize client interactions, achieving increased conversion rates.

Sep 2021 – Jan 2022 · 4 mos · Ottawa, Ontario, Canada

• Worked in a fast-paced SaaS startup environment to bridge business goals with scalable Azure-based data solutions. Delivered onboarding optimization, real-time analytics, and ML-powered feedback systems to drive user engagement and team agility.
• Designed BRDs, functional flows, and data specifications to translate gathered business and technical requirements into actionable onboarding models.
• Built scalable ETL pipelines using Python and Azure Data Factory, with Snowflake as the target warehouse, cutting data refresh times by 50%.
• Led A/B testing and uplift modeling using PySpark and Azure ML Studio, improving onboarding conversion rates by 18%.
• Engineered sentiment analysis pipelines with Azure Text Analytics API and Python-based NLP models, reducing feedback resolution time by 30%.
• Designed near real-time (NRT) data processing solutions using Azure Stream Analytics, Event Hub, and Service Bus Queue.
• Automated ingestion workflows with Azure Logic Apps, ensuring timely and accurate data delivery to downstream systems.

Feb 2021 – Apr 2021 · 2 mos · Ottawa, Ontario, Canada · Remote

• Designed and deployed forecasting dashboards and real-time anomaly detection pipelines using Azure tools and IoT data to enhance agricultural sustainability and reporting and Provide suggestions to reduce the carbon footprint of the agricultural business
• Developed internal SRS documentation to support grant compliance and sustainability audits.
• Partnered with the founder to transform data insights into a pitch deck, contributing to a successful seed round fundraising.
• Built interactive Power BI dashboards integrated with Azure services to forecast crop yield logistics with 35% accuracy, enhancing inventory and supply chain planning.
• Deployed real-time anomaly detection pipelines using Azure IoT Hub and Stream Analytics, reducing equipment downtime through early detection of sensor anomalies.
• Extracted and standardized global sustainability datasets using Python and Azure Data Lake, enabling accurate carbon footprint tracking and reporting.

Aug 2018 – Aug 2021 · 3 yrs · Chennai, Tamil Nadu, India

• Designed and optimized big data and BI pipelines for global commerce and retail clients, with progressive integration into Azure technologies. Migrated legacy solutions to cloud-native frameworks.
• Led big data engineering efforts using Hadoop, Spark, and SSIS, with progressive migration to Azure Data Factory, Event Hub, and Power BI reporting.
• Engineered real-time streaming pipelines using Apache Kafka and Spark, and transitioned to Azure Event Hub + Azure Databricks, enabling ingestion of 1M+ events/day and reducing fraud detection latency by 45%.
• Replaced SSIS/SSRS batch pipelines with Azure Data Factory (ADF) workflows and automated transformations using PySpark, improving historical report generation and regulatory compliance.
• Migrated on-prem Hive SQL and Hadoop workflows to Azure Synapse and Spark SQL, reducing data latency and eliminating daily SLA breach incidents due to delayed refreshes, cutting SLA breach incidents and improving query runtime by 30%.
• Migrated Hive-based Hadoop workloads to Azure Synapse Analytics (Preview) to evaluate performance improvements and cost efficiencies in a unified lakehouse architecture.
• Supported cloud migration planning with integration of PolyBase for external data queries and Azure HDInsight for compatibility with legacy Hadoop workloads.
• Leveraged Azure Blob Storage with PolyBase to enable external table reads in Synapse without full ingestion, streamlining integration of semi-structured files.
• Implemented Azure Monitor and Log Analytics for job performance tracking, achieving 99.8% uptime for batch and streaming workflows, custom job health-check alerts.
• Participated in early implementation of Azure Purview (Preview) for tracking data lineage and cataloging enterprise metadata across data warehouse assets.
• Designed and deployed Power BI dashboards connected to Azure SQL and Synapse, replacing Tableau for key clients and boosting business user adoption by 40%.