Data Engineer Supply Chain

Prasannakumar Kasindala

Data Engineer *************@*****.*** Linkedin 201-***-****

SUMMARY

Data Engineer with 4+ years of experience in building scalable data pipelines, cloud data platforms, and real- time streaming solutions. Specialized in AWS services (Glue, Redshift, S3) with strong hands-on experience in Snowflake, Databricks, Airflow, and DBT. Skilled at designing ETL/ELT pipelines, optimizing data warehouses, and applying governance best practices. Proven ability to deliver secure, cost-efficient, and business-focused data solutions for enterprise clients in retail and fintech. EDUCATION

Masters in Management Data Analytics, SPEC from Indiana Weslyean University, Dec 2024 SKILLS

Programming & Scripting: Python (Pandas, PySpark), SQL, Scala

Cloud Platforms: AWS (Glue, Redshift, S3, RDS), Snowflake, Databricks

Data Integration & Orchestration: Apache Airflow, DBT, AWS Step Functions, Kinesis

Data Warehousing & Modeling: Redshift, Snowflake, Star/Snowflake Schema, Data Vault

Streaming & Big Data: Apache Kafka, AWS Kinesis, Delta Lake

Automation & DevOps: Terraform, GitHub Actions, AWS CodePipeline

Data Security & Governance: IAM, GDPR, Data Masking, Compliance Standards

Visualization & BI: Amazon QuickSight, Tableau

EXPERIENCE

Data Engineer Kroger Feb 2024 – Current

Environment: AWS (Glue, Redshift, S3, RDS), Databricks, Airflow, Kafka, QuickSight

Designed and automated ETL pipelines in AWS Glue and PySpark to ingest retail transactions and supply chain data into Redshift for analytics and reporting.

Implemented Databricks with Delta Lake for advanced transformations, supporting scalable machine learning feature sets and reducing processing time by 30%.

Orchestrated workflows using Apache Airflow DAGs, improving pipeline reliability and reducing manual dependencies by 60%.

Built Kafka-based real-time ingestion pipelines, enabling near real-time visibility into store sales and inventory levels.

Partnered with BI teams to develop QuickSight dashboards that delivered actionable insights on product demand and supply chain efficiency.

Data Engineer Intuit Dec 2019 – Sep 2022

Environment: AWS (Glue, Redshift, S3), Snowflake, DBT, Kafka, Tableau

Migrated petabyte-scale datasets from legacy systems into Snowflake and Redshift, applying DBT models for standardized transformations and reusable data marts.

Developed ETL pipelines with Glue and PySpark to process structured/semi-structured data from APIs, S3, and RDS, improving ingestion throughput by 40%.

Built CDC pipelines with Kafka to replicate transactional data into Snowflake, enabling finance teams to access near real-time revenue metrics.

Optimized Snowflake cost and performance by tuning virtual warehouses, implementing clustering, and applying role-based access controls (RBAC).

Enhanced Redshift query performance via schema redesign, distribution/sort keys, and automated vacuuming, cutting query runtime by 30%.

Delivered interactive Tableau dashboards with self-service KPIs for finance and product teams, improving reporting turnaround by 50%.

Ensured compliance with IAM policies and GDPR by implementing secure access and data masking strategies.

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