Loading profile…
Loading profile…
Staff Data Engineer
Staff Data Engineer specializing in multi-cloud lakehouses, real-time streaming, and AI-ready data platforms that cut costs and speed insights.
United StatesStaff Data Engineer at Lateetud
Architected a multi-cloud lakehouse on AWS and Azure for real-time financial analytics, delivering CDC + Kafka + Spark Structured Streaming with sub-second latency. Reduced cloud costs 30%, improved pipeline MTTR by 60%, and enforced SOC 2 and GDPR compliance using Unity Catalog RBAC, PII masking, and automated data quality checks.
LakehouseSnowflakeDatabricksKafkaSpark Structured StreamingDBTUnity CatalogSOC IIGDPRTerraform
Designed a HIPAA-compliant lakehouse on Azure using ADLS Gen2, Databricks, and Snowflake, building batch/incremental/CDC pipelines into a medallion architecture. Implemented SCD Type 2 modeling and automated data quality frameworks, and delivered dbt semantic/metric layers for Power BI while reducing ML training cycle time by 40%.
Azure ADLS Gen2Databricks
Built cloud-native data pipelines on AWS using S3, Redshift, and Airflow to ingest application events, clickstream, and SaaS data at scale. Developed near real-time Kafka + Spark Structured Streaming for live product analytics, implemented dbt analytics engineering, and supported churn/LTV feature pipelines while reducing SLA breaches through more reliable ETL workflows.
AirflowAmazon RedshiftKafkaSpark Structured StreamingDBTA B TestingSalesforceStripeSegmentS3
Built ETL pipelines ingesting ERP, POS, and logistics data into a centralized warehouse for supply chain and retail analytics. Designed dimensional star-schema models, contributed to cloud migration, and implemented automated validation/reconciliation to reduce manual correction effort and improve operational reporting.
ETLDimensional ModelsStar SchemasData ValidationData ReconciliationSQLError HandlingLoggingData Warehouse
Profile sourced from Himalayas.