Browse categories
Explore
Fiverr Pro
English
$
USD
I will do etl, data pipeline, and data engineering projects
Enterprise data engineer building automated pipelines and optimizing SQL
Data Engineer | Python | SQL | ETL | Spark I build production-ready data pipelines, ETL workflows, and data solutions using Python, SQL, PySpark, Apache Spark, Airflow, Databricks, Snowflake, Trino, a...
About this Gig
Need a reliable ETL pipeline to automate your data workflow?
I will build a clean, scalable ETL/ELT pipeline using Python, SQL, Apache Spark/PySpark, and Airflow.
I can help you with:
- Extracting data from APIs, CSV/Excel files, and databases
- Data cleaning, transformation, validation, and deduplication
- ETL/ELT pipelines using Python and SQL
- PySpark/Spark processing for large datasets
- Airflow DAGs and workflow orchestration
- Loading data into PostgreSQL, SQL Server, Snowflake, Databricks, or other data platforms
- Incremental loads, scheduling, and pipeline optimization
- Debugging and improving existing pipelines
Youll receive clean, maintainable code, proper documentation, and a solution tailored to your requirements.
Have an existing pipeline that isn't working? I can troubleshoot and optimize it too.
Tools & Platforms:
Azure Data Factory
My Portfolio
Other Data Engineering Services I Offer
FAQ
What types of data sources can you work with?
I can work with SQL databases, CSV/Excel files, REST APIs, cloud storage, JSON data, and many other structured data sources.
Can you build Apache Airflow workflows?
Yes. I can create Airflow DAGs with scheduling, dependencies, retries, logging, and monitoring.
Do you support Apache Spark?
Yes. I build Spark pipelines for large-scale data processing, transformations, and ETL workloads.
Can you automate recurring data jobs?
Absolutely. I can create scheduled pipelines that automatically extract, transform, validate, and load data.
Can you deploy the pipeline?
Yes. Deployment assistance using Docker or cloud platforms can be included as a Gig Extra or in the Premium package.
