Gobinda Pokharel
@gobindapokharel
Senior data engineer building governed AWS/Azure/GCP Lakehouse pipelines and real-time fraud/AML analytics.
What I'm looking for
I architected hybrid cloud data platforms across AWS and Azure at JPMorgan Chase & Co., integrating core banking, payment platforms, card transaction feeds, and customer activity datasets with Databricks, Snowflake, ADLS Gen2, and enterprise streaming services. I designed lakehouse architectures with Databricks Delta Lake, Unity Catalog, and Snowflake to enable governed processing of transaction, payment, customer, and risk datasets, cutting reconciliation issues by 40%.
I’ve also built distributed batch and real-time streaming pipelines with PySpark, Spark Structured Streaming, Kafka, AWS Kinesis, and Azure Event Hubs—processing 7.5 TB of payment events daily—and standardized AML reporting and compliance transformations with dbt across 14+ banking domains. I automate anomaly detection and monitoring using Microsoft Fabric, Azure AI Foundry, Monte Carlo, CloudWatch, and Azure Monitor, and I migrate legacy Hadoop ETL into cloud-native architectures to reduce operational costs and improve reliability.
Experience
Work history, roles, and key accomplishments
Architected hybrid cloud data platforms across AWS and Azure, integrating core banking systems and payment platforms. Designed enterprise Lakehouse architecture using Databricks Delta Lake and Snowflake, reducing reconciliation issues by 40%.
Developed scalable ETL/ELT data pipelines using Apache Spark, PySpark, and SQL for transaction processing and fraud analytics. Built distributed batch and streaming data ingestion frameworks processing 4.2+ TB of financial data daily.
Developed and maintained scalable ETL pipelines using Apache Spark, Spark SQL, and Hive for processing insurance datasets. Built distributed data ingestion frameworks using Sqoop, Flume, and Kafka.
Education
Degrees, certifications, and relevant coursework
East Texas A&M University
Master of Science, Business Analytics
Master's of Science in Business Analytics from East Texas A&M University.
Tech stack
Software and tools used professionally
Amazon Redshift
Azure Synapse
Apache Spark
AWS Glue
Apache Flink
Azure RBAC
Amazon CloudWatch
Amazon S3
GitHub
Bitbucket
Kubernetes
AWS CodePipeline
Jenkins
GitHub Actions
PySpark
dbt
Sqoop
Hadoop
Yarn
Databricks
Microsoft Teams
Terraform
Azure DevOps
Jira
Java
Logstash
Kafka
Apache NiFi
Grafana
Kibana
Prometheus
Azure Monitor
Linux
Datadog
Trello
Elasticsearch
Azure Functions
Airflow
Apache Oozie
Time Analytics
Google BigQuery
SQL
Azure Blob Storage
ServiceNow
LangChain
Foundry
Monte Carlo
Delta Lake
Microsoft Fabric
Unity Catalog
Factory
Azure AI Foundry
Availability
Location
Authorized to work in
Job categories
Skills
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