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I will build a production ready etl data pipeline using AWS, airflow, and pyspark
Data Engineer, AWS, Apache Airflow, Spark, PostgreSQL, ETL
I am a Data Engineer and final-year Computer Science student with hands-on professional experience building scalable ETL pipelines and data architectures. I have worked at Cognetix.io on enterprise-gr...
About this Gig
Are you drowning in raw data with no reliable way to process it?
I build production-grade data pipelines that run automatically, scale with your data, and never break silently. No spaghetti scripts. No manual steps. Just clean, reliable data exactly where you need it.
What I Build
- ETL pipelines using Python and PySpark extract, transform, load, done
- Apache Airflow DAGs for fully automated, scheduled workflows
- Medallion Architecture pipelines (Bronze Silver Gold) with data quality at every layer
- AWS data platforms S3 data lake, Glue, EMR on EKS, IAM, Terraform
- Cloud ingestion pipelines from any source into PostgreSQL, MySQL, ClickHouse, or Supabase
- Fully containerised setups with Docker and Docker Compose
- One-command deployments with CI/CD no manual SSH, no runbooks
My Portfolio
FAQ
Q: What information do you need to get started?
A: Your data source (S3, API, database, CSV), your target destination, transformation requirements, and how often the pipeline should run.
Q: Can you work with my existing infrastructure?
A: Yes. Send me details and I will assess compatibility before we start.
Q: Do I need an AWS account?
A: For AWS-based work yes — you will need your own account. I can guide you through the setup if needed.
Q: Will I own the code?
A: Completely. All source code is handed over to you on delivery.
Q: Can you handle large datasets?
A: Yes. I use PySpark and EMR on EKS specifically because they are built for large-scale data processing.
Q: What if something breaks after delivery?
A: I offer post-delivery support. Message me and I will fix it.
