Sileman Shaik

About Sileman Shaik

I build data pipelines that process 18 million records in 5 minutes instead of 2.5 hours.Over the past 4 years at TCS, I\'ve specialized in cloud-native data engineering—migrating 20TB+ from Snowflake to BigQuery, designing real-time ETL workflows, and cutting cloud costs by 35% through smart optimizations.WHAT I DO- Build production-grade ETL/ELT pipelines on GCP and AWS- Optimize slow, expensive data workflows (my record: 97% runtime reduction)- Design scalable data architectures (batch + streaming)- Wrangle PySpark, SQL, Airflow, and cloud platforms dailyTECH I WORK WITH:Cloud: GCP (BigQuery, Dataflow, Composer)| AWS (Glue, Redshift, Lambda)Big Data: Apache Spark (PySpark), Apache Airflow, DatabricksLanguages: Python, SQLDatabases: BigQuery, Snowflake, Redshift, PostgreSQL, OracleRECENT WINS: Migrated 20TB data warehouse with zero downtime Re-engineered pipeline from 2.5h to 5min (saved 15 compute hours daily) Built 15+ production pipelines processing millions of recordsCERTIFICATIONS- AWS Certified Developer - Associate- Google Cloud Certified Associate Cloud EngineerI\'m currently seeking Data Engineer roles where I can design scalable data platforms from the ground up. If you\'re building something interesting with data, let\'s connect.Open to: Full-time roles | Remote or Hyderabad | Available immediately s••••••••@gmail.com

Experience

1. System Engineer

   Tata Consultancy Services

   Jul 2023 — Present

   Built 15+ production data pipelines on AWS and GCP. Led 20TB Snowflake-to-BigQuery migration. Optimized critical pipeline by 97%(2.5h → 5min), saving 15 compute hours daily.Key Achievements: Pipeline OptimizationRe-engineered Apache Airflow pipeline processing 18M records, reducing runtime from 2.5 hours to 5 minutes (97% improvement)Implemented Dataflow batch processing with optimized partition sizing and parallel workersImpact: Enabled real-time business decisions, saved ~₹50K/month in compute costs Cloud Migration & ArchitectureLed 20TB+ Snowflake to BigQuery migration using GCS, Dataflow, and Cloud Composer with zero data lossDesigned ETL architecture supporting seamless warehouse consolidationReduced query latency by 60% through BigQuery partitioning and clustering AWS Data EngineeringBuilt 15+ production ETL pipelines using AWS Glue, S3, Athena, Step Functions (40% efficiency gain)Created near real-time ingestion workflows with Lambda and Step Functions (30% latency reduction)Implemented Redshift optimization strategies reducing cloud costs by 25% Modern Data PlatformsMigrated pipelines to Databricks with Medallion Architecture (Bronze-Silver-Gold)Leveraged Delta Lake for ACID transactions and data versioningEnhanced data quality through structured data layering Automation & Best PracticesAutomated PySpark and SQL transformations for batch and streaming pipelinesBuilt CI/CD pipelines using GitHub for automated deploymentImplemented data quality automation ensuring 99%+ accuracyTech Stack: AWS (Glue, Redshift, S3, Lambda, Step Functions)| GCP (BigQuery, Dataflow, Cloud Composer, GCS)| Databricks | Python | PySpark | SQL | Airflow | Snowflake

Education

• Vignan's Foundation for Science, Technology & Research

  Computer Science