Bharath Surampudi

Data Engineer | AWS Solutions | Java & Python Expert
📍 Sydney, Australia | bharathsurampudi@gmail.com | 📞 0410 638 861
LinkedIn | GitHub

Professional Summary

Data Engineer with strong commercial experience in building secure, scalable data pipelines at Mastercard. Expert in orchestrating complex data flows using Apache NiFi and Java, with a focus on reliability and compliance in regulated financial environments. Proficient in the Modern Data Stack (AWS, Airflow, dbt), bridging the gap between rigorous software engineering practices (CI/CD, Testing) and cloud-native data architecture.

Technical Skills

Cloud & Platforms AWS (S3, Glue, Redshift, EMR, Lambda), Terraform.
Data Engineering Apache NiFi, Airflow, dbt, Spark, Kafka.
Programming Python (Pandas, PySpark), Java (Spring Boot), SQL.
DevOps & Tools Docker, Git, Jenkins, Splunk, CI/CD.

Professional Experience

Software Engineer II (Data Engineering & Payments)
Mastercard, Sydney Nov 2021 – Present
  • Enterprise Data Pipeline Orchestration: Architected and maintained end-to-end data ingestion pipelines using Apache NiFi. Managed secure routing of sensitive payment data across global regions, ensuring 99.9% availability.
  • API-Driven Data Integration: Developed robust Java Spring Boot APIs to serve high-volume transaction data to downstream analytics and payment gateways.
  • Data Reliability Engineering: Automated log monitoring frameworks using Splunk, creating real-time dashboards to detect data transfer bottlenecks.
  • Cross-Functional ETL Optimization: Collaborated with data architecture teams to modernize legacy ETL jobs, improving data throughput for financial reporting systems.
Software Engineer
Neau Collective, Sydney Mar 2021 – Nov 2021
  • Automated Data Ingestion: Developed Python automation scripts to extract and consolidate data from Shopify and marketing APIs, reducing manual reporting effort by 70%.
  • Data Warehousing Support: Integrated distinct data sources (Sales, Marketing, Accounting) to build unified datasets for business intelligence.

Cloud & Data Engineering Projects

End-to-End Serverless Data Lakehouse on AWS
Stack: AWS S3, Glue, Redshift, dbt, Terraform
  • Designed a cloud-native data platform to ingest raw data into S3, process it with AWS Glue (PySpark), and load it into Redshift.
  • Implemented dbt for in-warehouse data transformation and automated schema testing.
  • Provisioned all infrastructure using Terraform (Infrastructure as Code).
Scalable Data Orchestration Platform
Stack: Apache Airflow, Python, Docker, PostgreSQL
  • Built a containerized Airflow environment to schedule complex dependent data workflows (DAGs).
  • Automated ingestion from external APIs with dynamic DAG generation and configured SNS alerts for pipeline failures.

Certifications

Education

Master of Information Technology | UNSW, Sydney (2019 – 2021)
Specialization: Artificial Intelligence & Database Systems
Bachelor of Technology in Computer Science | Vellore Institute of Technology (2014 – 2018)