Akash Anande

About Akash Anande

As a Senior Data Engineer with over nine years of experience, I specialize in building scalable big data pipelines, cloud-native solutions, and AI-ready infrastructures for global enterprises. I currently lead data engineering initiatives at Matrix IFS, where I engineered a cost-optimization platform for Nomura (US investment bank), cutting AWS cloud expenses through optimized Glue, EMR, and Lambda orchestration.Previously at Impetus Technologies, I developed a machine learning recommendation engine for Deluxe Corp, using AWS Glue, SageMaker, and Step Functions, increasing system uptime. I also delivered analytics platforms for United Airlines, reducing flight boarding time through efficient ETL and data model design in Redshift and Glue.I bring hands-on expertise in AWS, PySpark, Apache Spark, Terraform, Jenkins, Hive, Sqoop, HDFS, and Kubernetes, with a strong focus on data quality, performance tuning, and automation. From regulatory platforms at Mits Global to trade prediction pipelines at BNP Paribas, I’ve consistently delivered low-latency, high-accuracy systems for finance and compliance at scale.Passionate about AI-enabling infrastructure, data observability, and cost-effective cloud engineering, I am committed to transforming raw data into actionable intelligence through robust, modern architectures.

Experience

1. Senior Data Engineer

   Matrix

   Jul 2024 — Present

   Implemented critical improvements in pipeline reliability, increasing system uptime by leveraging checkpointing and addressing infrequent job timeouts, ensuring seamless operations for sensitive compliance tasks.• Led the migration of infrastructure from AWS CloudFormation to Infrastructure-as-Code (IaC) using Terraform, enabling Nomura\'s data pipelines to become cloud-agnostic and future-proof for potential migration to other cloud providers.• Played a key role in designing the overall deployment strategy for Nomura\'s cloud architecture, defining the approach for CI/CD processes, while collaborating with another team member who implemented the pipeline automation using Jenkins, enhancing scalability and flexibility.• Delivered significant cost reductions and performance optimizations by refining PySpark-based processing workflows, ensuring resource efficiency and enabling smoother operations with reduced overhead.

Education

• Woolf

  Master of Science - MS, Computer Science

Skills

• Python