class DataEngineer:
def __init__(self):
self.name = "Nikesh Chavhan"
self.role = "Data Engineer | ML Engineer | Data Scientist"
self.location = "Nagpur, India 🇮🇳"
self.experience = "3+ years"
self.specialties = [
"Real-Time Data Pipelines",
"Cloud ETL Workflows",
"Streaming Analytics",
"Machine Learning & Predictive Analytics",
"GenAI & RAG Systems"
]
self.currently_working_on = [
"LLM-Powered Pipelines",
"Real-Time Fraud Detection with ML",
"Auto-Scaling ETL Systems",
"Advanced ML Model Deployment"
]
self.certifications = [
"Data Engineering Associate (AWS)",
"Data Engineering Professional (GCP)",
"Meta Database Engineer",
"Data Scientist Professional (Datacamp)"
]
def say_hi(self):
print("Thanks for dropping by! Let's build scalable data solutions together 🚀")
me = DataEngineer()
me.say_hi()- � Real-Time Data Engineering: Building streaming pipelines with Kafka, Spark Streaming, and AWS Kinesis
- ☁️ Cloud ETL Workflows: Designing scalable data pipelines on AWS (S3, Glue, Lambda, Redshift, EMR)
- 🤖 GenAI & RAG Systems: Developing LLM-powered pipelines with semantic search and prompt engineering
- 📊 Big Data Processing: Apache Spark (Batch & Streaming), Flink, distributed computing at scale
- �️ Data Infrastructure: Airflow orchestration, DBT transformations, Terraform IaC
- 📈 Analytics & Monitoring: ELK Stack, Prometheus, Grafana, Streamlit, Tableau dashboards
- 🔐 Fraud Detection: ML-based real-time anomaly detection with XGBoost and streaming analytics
- � AWS Data Engineering Associate (In Progress)
- 📜 Google Cloud Data Engineering Professional (In Progress)
- 🎓 Meta Database Engineer Professional - Coursera
- 🎓 Data Scientist Professional - Datacamp
"Building scalable data pipelines that transform raw data into actionable insights." 🚀💡
Open to exciting Data Engineering opportunities and collaborations!
