Teja Naidu Chintha

About Teja Naidu Chintha

Driven by a passion for transforming data into reliable, scalable solutions, I specialize in designing, optimizing, and automating data pipelines for real-time and batch environments across cloud platforms. With 4+ years of hands-on experience in Spark, Airflow, Azure, AWS, and GCP, I have a proven record of reducing processing times by up to 50%, driving 40% cost savings, and ensuring compliance with SOX, GLBA, and GDPR. I thrive on building ETL/ELT pipelines that process terabytes of multi-source data while maintaining the highest standards for data quality, lineage, and security. My expertise spans Delta Lake optimization, workflow orchestration, CI/CD automation, and robust data validation frameworks like Great Expectations and PyDeequ. I am equally at home fine-tuning Spark clusters for high throughput or integrating Kafka and Event Hubs for event-driven architectures. Beyond core engineering, I’m dedicated to automation and infrastructure-as-code, leveraging Terraform, Docker, and Kubernetes to deliver high-availability systems and fast, reliable releases. I enjoy collaborating with cross-functional teams, mentoring peers, and tackling complex challenges in fast-paced environments. If you’re looking for a results-focused data engineer who combines technical rigor with creative problem-solving, let’s connect.

Experience

1. Data Engineering Analyst Ii

   U.S. Bank

   Oct 2024 — Present · Dallas, TX, US

   Built Spark/Scala ETL pipelines for 50TB+ of batch data from Synapse, Kafka, and REST APIs, reducing processing time by 35%. Optimized Azure Data Factory for both batch and streaming, automating job schedules to exceed 99% SLA compliance metrics. Automated Spark orchestration with Airflow DAGs, integrating alerting and retries, eliminating 80% of manual deployment steps. Reduced cloud costs 40% through Delta Lake partitioning, compaction, auto-scaling, with zero SLA breaches and performanceloss. Applied Apache Atlas for column-level data lineage and automated auditing, enabling SOX and GLBA compliance for all keyassets. Utilized Great Expectations & PyDeequ to automate data validation, blocking bad loads, detecting 99% anomalies before ingestion. Enhanced Spark throughput 50% by tuning executor/memory config, monitoring YARN metrics, and resolving cluster bottlenecks.

Education

• Indiana University Bloomington

  Master's degree

• SRM University (Sri Ramaswamy Memorial University)

  Bachelor of Technology - BTech