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Ashish Rogannagari

@ashishrogannagari

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Data Engineer with expertise in cloud-native data pipelines.

United States

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What I'm looking for

I am looking for a role that fosters collaboration, innovation, and growth opportunities in data engineering.

I am a Data Engineer with over 4 years of experience in building and optimizing scalable, cloud-native data pipelines and analytics infrastructure across both startups and enterprise environments. My expertise lies in architecting robust ETL workflows using Python, SQL, and big data technologies such as Spark and Hadoop. I have a strong command over cloud services like AWS and Snowflake, and I utilize data orchestration frameworks such as Dagster and dbt to deliver timely and reliable data for analytics and machine learning use cases.

Throughout my career, I have successfully automated data ingestion and transformation processes using infrastructure-as-code (Terraform), Dockerized deployments, and CI/CD pipelines, which has significantly enhanced productivity and reduced operational overhead. I am adept at collaborating with cross-functional teams to support AI/ML model deployments, ensuring data observability, and upholding DataOps best practices. Currently, I am pursuing a Master’s in Data Analytics & Visualization, further strengthening my blend of technical engineering and analytical capabilities.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Chord

Jan 2024 - Present (1 year 6 months)

Designed and optimized complex SQL queries for large-scale datasets, enhancing data retrieval performance and reliability across analytical pipelines. Engineered custom ingestion pipelines using Data Load Tool (DLT) and Dagster, enabling seamless ingestion of data from third-party APIs and internal data sources into Snowflake using Snowpipe, Streams, and Tasks.

SQL Python BASH Dagster Snowflake Snowpipe AWS ECS AWS Lambda AWS S3 AWS IAM DBT Terraform Docker GitHub Actions Jenkins CircleCi DataOps

Data Engineer

Raft

Sep 2019 - May 2023 (3 years 8 months)

Designed and implemented scalable ETL pipelines using Hadoop and Apache Spark, optimizing batch processing of large volumes of structured and unstructured data. Built robust data ingestion frameworks integrating HBase, Cassandra, and Google Bigtable, enabling real-time analytics and reducing query latency.

Hadoop Apache Spark ETL HBase Cassandra BigTable Python Java AWS S3 EMR Glue Lambda Teradata Greenplum Vertica Terraform CloudFormation GitHub

Education

Degrees, certifications, and relevant coursework

Yeshiva University - Katz School of Science and Health

Master of Science, Data Analytics & Visualization

Currently pursuing a Master’s in Data Analytics & Visualization, further strengthening a blend of technical engineering and analytical capabilities. This program focuses on advanced data analysis techniques and data visualization principles.