Shashank Reddy
Data Platform Engineer @Spreetail
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WORK HISTORY
Data Platform Engineer @Spreetail
Austin, TX, US
Designed and maintained 18+ batch and near–real-time pipelines integrating data from 10+ sources (APIs, relational databases, flat files) into an AWS based centralized cloud data lake/warehouse to support analytics and scheduled reporting. • Built and productionized a Snowflake-based (Inbound data) API data platform from scratch, including API ingestion, CDC processing, and automation that runs daily, delivering curated tables/views and stored procedures to support auditable, reproducible reporting.• Built scalable ETL/ELT workflows using Databricks (Spark) with Docker + Kubernetes, reducing average transformation runtime by 30% and improving job success rate to 99%+.• Implemented ingestion and automation patterns using AWS Lambda, Kinesis, S3, and DynamoDB, processing ~250GB/day and consistently meeting daily data availability SLAs by 8:00 AM for reporting cycles.• Implemented Snowflake access controls using role-based access (RBAC) patterns and least-privilege design to support governed access to curated datasets.• Built medallion architecture (Bronze/Silver/Gold) transformations in Snowflake for marketplace traffic feeds (multiple sources); implemented a Bronze/Silver/Gold pattern for advertising data in Databricks and published curated outputs into Snowflake for downstream reporting. • Automated ingestion from SharePoint/network sources into Snowflake using Power Automate, Power BI, Databricks, reducing manual effort by 8–10 hours/week and improving repeatability and governance.
EDUCATION
CVR College of Engineering, Hyderabad
Bachelor of Technology - BTech, Computer Science
The University of Texas at Arlington
Master of computer science, Computer Science
ABOUT SHASHANK REDDY
I’m a Data Engineer with 5+ years of experience building and maintaining cloud-based data platforms that support analytics, reporting, and research use cases. My focus is designing scalable ELT/ETL pipelines, delivering trusted datasets for BI, and improving reliability through data quality, documentation, and governance.In my current role at Spreetail, I build batch and near–real-time pipelines using Snowflake, dbt, Databricks (Spark), and AWS, integrating data from APIs, databases, and files into curated tables/views used for scheduled reporting. I’ve implemented automation patterns, access controls, and repeatable transformations that improved runtime, stability, and auditability.Previously, I built migration frameworks and orchestrated pipelines with Azure Data Factory, Databricks, Kafka, and Elasticsearch, improving latency and search performance for analytics consumers. I also have experience designing curated warehouse datasets and running validation/reconciliation checks to improve trust in reporting.I’m especially interested in roles with universities, non-profits, research organizations, and healthcare teams, where data accuracy, reproducibility, and governance matter. I work well with cross-functional stakeholders (analytics, finance, research, operations) to translate requirements into reliable data products.
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