Devanandam Seeman

Achievements/Tasks: -- Architected and delivered scalable cloud data platforms using Azure Synapse, Databricks, Snowflake, Azure DataLake, AWS, and SQL Server, enabling enterprise-wide analytics and data-driven decision-making. -- Designed and implemented a Medallion Lakehouse architecture, minimizing Power BI processing bottlenecks bypre-calculating complex aggregations in the Gold layer to boost data freshness and report rendering times by30%. -- Developed reusable metadata-driven ingestion frameworks processing 20M+ records daily, significantlyaccelerating data onboarding and improving analytics delivery efficiency. -- Optimized PySpark-based ETL/ ELT workloads, achieving a 40% reduction in pipeline execution time, loweringcloud compute overhead, and enabling faster access to business insights. -- Partnered with business stakeholders, analysts, and architects to translate strategic requirements into scalable,production-ready data solutions supporting reporting and operational monitoring. -- Implemented cloud cost-optimization strategies for Azure Synapse Spark pools and Azure Data Factory (ADF)pipelines, reducing monthly cloud compute spend by 15% without impacting data processing performance. -- Delivered curated datasets powering 20+ distinct analytical use cases across cross-functional teams, eliminatingdata silos and establishing a single source of truth for corporate reporting. -- Led a team of 3 offshore data engineering consultants and mentored newly onboarded engineers, improvingknowledge transfer, delivery consistency, and project scalability. -- Contributed to cloud modernization initiatives by migrating legacy Oracle workloads to AWS and SQL Serverplatforms, while establishing automated data quality and monitoring frameworks to enhance governancecompliance.

Achievements/Tasks: -- Developed and optimized PySpark and SQL-based ETL pipelines on Azure Databricks and Azure Data Lake Storage(ADLS), enabling the scalable processing of 1+TB of enterprise data for downstream analytics. -- Integrated, cleansed, and transformed large-scale enterprise datasets, improving data consistency andaccessibility for business users and downstream applications. -- Engineered automated validation controls that delivered a 25% improvement in data matching accuracy whileslashing pipeline processing time from 2 hours to under 30 minutes. -- Collaborated with cross-functional teams to streamline CI/ CD deployment, troubleshooting, and tuning of clouddata solutions, ensuring the uninterrupted delivery of analytics-ready datasets.

Achievements/Tasks: -- Designed and maintained enterprise ETL workflows integrating data from multiple disparate legacy systems,ensuring consistent data availability for operational processes and daily business reporting. -- Developed SQL-based automation solutions, stored procedures, and batch processing workflows to streamlinedata integration reliability and minimize manual intervention. -- Optimized database performance through targeted query tuning, execution plan analysis, and indexing strategies,achieving a 40% reduction in execution time for business-critical workloads. -- Improved data consistency and reporting accuracy by implementing automated data cleansing and reconciliationscripts, establishing trusted data baselines across cross-functional teams. -- Collaborated with cross-functional stakeholders across the full software development lifecycle (SDLC)—includingtesting, deployment, and production support—within Agile environments.