Profile
Senior Data Engineer with experience of designing and delivering scalable data platforms, real-time streaming pipelines and production-grade AI systems. I work across PySpark, Databricks Lakehouse, Delta Lake, Apache Kafka and AWS, and I build with Generative AI — fine-tuning open LLMs with LoRA/QLoRA and shipping LangChain + FAISS RAG pipelines. I created Datainteg.io, an open-source data engineering lab, and I teach everything I build on my YouTube channel.
Two disciplines
Data engineering
PySparkApache SparkDatabricksDelta LakeApache KafkaAWS GlueAWS EMRAmazon S3AWS LambdaSnowflakeApache AirflowPythonSQLPostgreSQLMongoDBFastAPIDocker
AWS Glue
Amazon S3
AWS LambdaAI engineering
LangChainLlamaIndexRAGFAISSLoRA / QLoRAMLflow
How I work
- Correctness first: a pipeline that is fast but occasionally wrong is a liability, not an asset.
- Make the boundaries explicit — grains, contracts, commit points and ownership.
- Separate what is verified from what is supplied and what is proposed. Evidence over claims.
- Teach what you build. If you cannot explain a trade-off simply, you do not understand it yet.