Akash P

About Akash P

Senior Data Engineer with 10+ years of experience designing and optimizing large-scale data platforms, specializing in cloud-native data engineering on Google Cloud Platform. I build high-performance batch and real-time data pipelines that enable analytics, reporting, fraud detection, and ML use cases across financial and healthcare domains.I have deep hands-on expertise across the GCP ecosystem including BigQuery, Cloud Dataflow, Cloud Dataproc, Cloud Composer, Cloud Pub/Sub, and Cloud Storage. My work consistently focuses on scalability, performance optimization, cost efficiency, and production reliability.What I Do Best:• Architect scalable ETL/ELT pipelines (batch + streaming)• Build high-throughput real-time processing systems using Pub/Sub & Dataflow• Design optimized BigQuery data models with partitioning & materialized views• Develop Spark workloads on Dataproc with performance tuning• Implement CI/CD for automated, reliable data deployments• Enforce IAM governance, data security, and monitoring best practices• Optimize GCP costs through resource tuning, storage lifecycle management, and slot planningThroughout my career at Wells Fargo, American Express, Humana, and Allstate, I have: Reduced data latency by up to 60% Improved query performance by 50–80% Lowered infrastructure costs by 25–35% Automated deployments from days to hours Designed hybrid streaming + batch architectures handling TB-scale workloadsI thrive in fast-paced, enterprise environments where data reliability, governance, and performance directly impact business outcomes. I collaborate closely with analytics, ML, and application teams to deliver secure, production-grade data platforms.Currently open to Senior/Lead GCP Data Engineering opportunities in Tampa or Dallas.Let’s connect to build scalable, intelligent data platforms.

Experience

1. Senior Data Engineer San Francisco, Ca February 2023 Present

   Wells Fargo

   Feb 2023 — Present · San Francisco, CA, US

   I build and support near real-time and batch data pipelines on AWS, including Spark Streaming jobs reading from S3 and Databricks Structured Streaming pipelines consuming Kafka topics and landing curated outputs into Delta Lake using merge/upsert patterns. I orchestrate end-to-end workflows using Airflow DAGs with dependency management, sensors, retries, SLAs, and monitoring to improve pipeline reliability and operational stability. I also develop serverless ingestion and automation patterns using AWS Glue, Lambda, and Step Functions, and implement data quality and validation checks to ensure consistency and completeness across feeds. In addition, I work on high-volume real-time processing with Flink integrated with Kafka, and support low-latency access layers using Aerospike including performance tuning and replication/fault tolerance. I contribute to modeling and analytics enablement using Redshift and support modernization/migration initiatives such as moving workloads to EMR and strengthening observability through CloudWatch. I also automate deployment and configuration using Ansible, support CI/CD with Jenkins, and improve platform performance for downstream services including search improvements using Solr.