Prayag Nikul Purani

Data Engineer with 3+ years of experience designing scalable data platforms and building high-performance analytics pipelines across fintech and telecom environments. Experienced in distributed data processing, real-time streaming, and cloud-native lakehouse architectures using Apache Spark, Kafka, and Flink. Skilled in developing reliable ETL and ELT frameworks, transforming large-scale transactional datasets, and optimizing analytical query performance.

Proven ability to support fraud analytics, revenue assurance, and business intelligence initiatives by delivering trusted, well-governed datasets and resilient data infrastructure that enable faster insights and data-driven decision making.

Data Engineer - Chime - Remote, USA

(2025-01)

• Engineered real-time financial transaction ingestion pipelines using Apache Flink and Google Cloud Pub/Sub, processing nearly twelve million daily card events into BigQuery datasets supporting fraud monitoring and risk analytics.
• Developed scalable ELT transformations using Trino and Apache Iceberg tables stored on Google Cloud Storage, consolidating twenty fintech transaction sources into standardized datasets for regulatory reporting.
• Implemented automated data quality monitoring frameworks using Great Expectations, validating financial event schemas and ensuring transaction consistency across streaming pipelines, reducing downstream reconciliation discrepancies by twenty-four percent.
• Optimized analytical performance of large BigQuery datasets by redesigning partitioning strategies and query patterns, decreasing average execution time for fraud detection analytics workloads by nearly twenty-nine percent.
• Designed Kubernetes-based containerized workflows integrated with Git-driven CI/CD pipelines, automating deployment of 25+ production data pipelines and reducing engineering release cycles by 40%.
• Developed curated analytical datasets powering 30+ Power BI and Tableau dashboards, enabling product and risk teams to monitor key financial metrics and improving reporting turnaround by 35%.
• Implemented data governance frameworks including lineage tracking and metadata cataloging across 50+ financial data pipelines, improving audit traceability and regulatory reporting readiness.

Data Engineer - LTIMindtree - Remote, India

(2021-08 - 2023-07)

• Built distributed telecom event ingestion pipelines using Apache Kafka and Apache NiFi, capturing nearly eight million daily network records and storing curated datasets within Azure Data Lake Storage.
• Engineered scalable batch processing workflows using Apache Spark with Scala on Databricks, transforming multi-source telecom usage data and reducing nightly processing latency across enterprise reporting systems.
• Designed dimensional data warehouse models in Azure Synapse Analytics using star schema architecture supporting 10+ telecom analytics dashboards and improving query performance by 30%.
• Implemented ELT transformation workflows using dbt and SQL pipelines, integrating over fifteen telecom billing datasets into structured warehouse tables supporting revenue assurance and customer analytics.
• Developed orchestration workflows using Apache Airflow scheduling and workflow automation, improving reliability of large-scale batch data processing pipelines across multiple telecom operational systems.
• Implemented Delta Lake lakehouse architecture on Azure storage environments, optimizing partition strategies and improving query performance for high-volume telecom analytical datasets by approximately twenty-six percent.
• Automated infrastructure provisioning using Docker and Terraform, standardizing deployment across 15+ data engineering environments and reducing environment setup time by 50%.

Master of Science - Data Analytics - San Jose state university (2023-08 - 2025-05)

Bachelor of Science - Computer science - Vellore institute of technology (2019-07 - 2023-04)