ARPIT TRIVEDI

• Owned quarterly roadmaps for 15+ concurrent data initiatives, aligning product, analytics, and leadership stakeholders, and delivering predictable releases across 6+ business units with 99% platform uptime, supported by AI-assisted planning and delivery tracking.
• Directed the end-to-end migration of 800+ in-house data workflows to Snowflake, collaborating with cross-functional teams to deliver a secure, scalable, and high-performing cloud data platform.
• Established platform governance and engineering standards across 150+ pipelines and 4,000+ datasets, improving reliability by 35% and optimizing cloud spend by 25% using AI-driven quality monitoring and cost insights.
• Architected a centralized Lakehouse platform using Medallion Architecture, enabling scalable, analytics-, and AI-ready data processing.
• Built scalable ingestion frameworks using JDBC, Debezium, Kafka, and Spark, reducing latency by 40%, maintaining 99.9% uptime, and supporting near-real-time reporting across 5+ business units.
• Optimized analytics workloads with Trino, Iceberg, and dbt on S3, reducing query costs by 85%, and enabling faster experimentation for analytics and AI use cases.

• Collaborated with cross-functional teams to define project requirements and specifications.

• Built a scalable user feature store using Spark, powering real-time, personalized song recommendations for 50M+ users, and actively leveraged by the Data Science team.
• Led data warehouse modernization (Redshift, BigQuery) to enhance reporting and decision-making.
• Led the implementation of GDPR-compliant data governance and IAM controls, and developed audit pipelines with Deequ and Great Expectations, reducing data loss by 30% and enabling proactive alerts for quality anomalies across critical systems.
• Collaborated with engineering, product, and analytics teams to drive data-driven strategies and innovation.
• Generated engineering documentation and oversaw design projects.

• Led data platform migration from in-house to AWS Cloud, migrating 100+ TB of data to Glacier and 300+ ETL jobs to the new platform.
• Built and led a 10-member team (DAAS & Platform), optimizing OLAPs, data models, and ensuring a scalable, production-grade platform.
• Designed HLDs & LLDs for system performance, Apache Atlas (Lineage), GDS, logging, schema generation, and volumetric analysis.

• Developed Big Data pipelines for clients, enabling scalable feature creation and analytics.
• Created Hive tables to manage structured data from UNIX, Oracle, and multiple portfolios, optimizing storage and retrieval.
• Built BI reports using multi-threaded Sqoop, exporting data to HDFS and Hive for analysis.