Parth Anshu

I’m an experienced Senior Data Engineer with 6+ years designing, developing, and optimizing scalable ETL/ELT pipelines, data warehouses, and data lakes for enterprise analytics and reporting. I focus on building high-performance data platforms that improve business intelligence, operational efficiency, and large-scale processing.

At Deloitte, I designed and deployed cloud-native architectures using Amazon S3, AWS Glue, and governed analytics with AWS Lake Formation and Collibra metadata. I engineered real-time transaction monitoring with Kinesis, Lambda, and Redshift, orchestrated end-to-end workflows with Apache Airflow, and delivered scalable batch processing using Apache Spark and PySpark on EMR.

I also build streaming and real-time solutions—leveraging Apache Kafka/Kafka Streams, Spark Streaming, and Kubernetes-based deployments—while keeping data trustworthy through frameworks like Great Expectations and dbt testing. I’ve implemented versioned analytics using Delta Lake and Apache Iceberg, and optimized query performance using Parquet, partition pruning, and Redshift/Athena improvements.

Previously at Cognizant, I developed GCP-based pipelines using Apache Beam and Dataflow, designed dimensional models using Star Schema, and delivered healthcare analytics workflows while maintaining strong compliance. Across both roles, I prioritize automation (Terraform, CI/CD with Jenkins/GitHub Actions), secure access (OAuth2/JWT), and observability (CloudWatch/Prometheus/Grafana/ELK) so teams can move faster with confidence.