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I will build efficient, scalable data pipelines
Estados Unidos
Hablo Inglés
Experienced Software and Data Engineer
I have five years of industry experience in programming, data science, and machine learning. I have built projects involving data pipelines, predictive models, and automation tools. With expertise in ...
Acerca de este Servicio
I will build custom data pipelines and ETL workflows to automate your data extraction, transformation, and loading processes. Using tools like Python, SQL, AWS, and Apache Airflow, I create efficient, scalable solutions tailored to your needs.
I integrate data from databases (PostgreSQL, MySQL, etc.), APIs, cloud storage (S3), and flat files (CSV, JSON, etc.) to streamline your operations and enable better decision-making.
Services:
- Data source integration (DBs, APIs, S3, CSV, JSON)
- API automation (optional add-on)
- Data cleaning & transformation (Python, SQL)
- Cloud deployment & orchestration (AWS, Airflow)
- Clean, reusable code with documentation
Packages:
- Quick Start Package: Simple ETL with 1 data source & basic transformations
- Multi-Domain Integration: Automated workflow w/ API integration & advanced transformations
- Complete Cloud Integration: Production-ready pipeline w/ cloud deployment, logging, & error handling
Revision Policy: Revisions follow package terms and cover adjustments to the delivered pipeline, not scope changes.
Message me today to automate your data processes!
FAQ
What do you need to integrate with my current systems?
I’ll need access to your data sources (e.g., API keys, database/cloud credentials) and info on your current system (data formats, tech stack, workflows). Not sure what you need? Message me, and I’ll guide you through it!
Can you deploy the pipeline to the cloud (e.g., AWS, GCP)?
Yes! I can deploy your ETL pipeline to cloud platforms like AWS or GCP and set up orchestration tools such as Apache Airflow or AWS Lambda (depending on your project needs).
Can the solution be scaled later as my data grows?
Absolutely. I build pipelines with scalability in mind, making it easy to add new data sources, handle larger datasets, or upgrade to distributed processing (e.g., Spark, Airflow) as your needs evolve.
