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I will be big data engineer for hadoop, spark, pyspark, java, scala, machine learning
Senior Data Engineer and Data Scientist
Senior Data Engineer and Scientist with 15+ years of experience in Hadoop, Spark, PySpark,
SQL, data pipelines and Machine Learning. I specialize in building scalable big
data solutions, data warehous...
About this Gig
Do you need a seasoned Big Data Engineer to handle complex data pipelines, analytics, or predictive modeling? You're in the right place!
With over 15 years of experience in data engineering, analytics, and software development, I bring deep expertise in Big Data technologies, Machine Learning, and cloud platforms (AWS, Azure, GCP).
Services I Offer:
- Big Data Solutions using Hadoop, Spark, PySpark, Hive, Pig, HBase
- ETL pipelines using SSIS, Talend, Airflow
- Real-time data streaming using Kafka, Flume, Logstash
- Data warehouse development (Star/Snowflake schema, SSAS, SSRS)
- Machine Learning & Deep Learning (Scikit-learn, TensorFlow, Keras, NLP, CNNs)
- Predictive models (Classification, Regression, Recommender Systems)
- Cloud-based data engineering (Azure Data Factory, AWS EMR, GCP BigQuery)
- Visualization using Power BI, Tableau
Tools & Technologies:
Spark, Hadoop, Hive, Pig, Kafka, Flume, PySpark, Java, Scala, Python, SSIS, SQL Server, Azure, AWS, GCP, Power BI, Tableau, TensorFlow, Keras
Why Choose Me?
- Microsoft & Oracle Certified (MCSE, OCP, MCSD)
- 8+ years in Hadoop Ecosystem
- Over 25 real-world big data/ML projects delivered
- Reliable, fast communication and delivery
My Portfolio
FAQ
What information do you need from me to start?
To get started, I’ll need access to your data sources (or sample data), a brief description of your business goals, and any specific requirements like expected output format (e.g., dashboards, CSV reports, API endpoints). For large projects, a short discovery call is recommended.
Do you work with live/production data?
Yes, I have extensive experience building and managing production-grade data pipelines. However, I always recommend starting with a staging or sampled environment to validate the logic before moving to full production deployment.
