I will build python, etl data pipelines and automate data workflows
Data Engineer
About this Gig
Are you looking for robust, scalable, and automated ETL/ELT data pipelines? I help businesses extract, clean, transform, and load their data seamlessly into modern databases and cloud data warehouses.
With 2+ years of hands-on Data Engineering experience, I specialize in building reliable, fault-tolerant data workflows using Python, SQL, and AWS.
What I Can Do For You:
Custom ETL/ELT Pipelines: Automated ingestion from REST APIs, webhooks, databases, or flat files (CSV/JSON/Parquet).
Cloud Data Warehousing: Schema design, modeling, and loading into Snowflake, AWS
Query Optimization & Tuning: Speeding up slow SQL queries and fixing performance bottlenecks.
Workflow Orchestration: Scheduling, dependency mapping, and automated alerts using Apache Airflow.
Real-Time Data Streaming: Near real-time pipelines using Apache Kafka.
Data Quality & Automated Tests: Implementing validation checks and pytest unit tests to prevent data corruption.
Tech Stack & Tools:
Languages: Python, SQL
Databases & Warehouses: Snowflake, MySQL
Cloud & Big Data: AWS (S3), Kafka, Airflow
Please message me with your data format and project requirements before placing an order!
Destination Platform:
Snowflake
•
Amazon S3
Tools & Platforms:
Other
FAQ
What details do you need from me before getting started?
Please provide a brief overview of your project requirements, sample input data files or schemas, target database/destination details, and necessary API credentials or access permissions.
What tools and technologies do you specialize in?
I specialize in Python and SQL for building automated data ingestion, transformation scripts, and database workflows. I work extensively with REST APIs, flat files (CSV, JSON, Excel), and relational databases.
What if my requirements do not fit the pre-set gig packages?
No problem. Every data architecture is unique. Send me a direct message explaining your setup, and I will create a tailored Custom Offer based on your exact scope and timeline.
How do you ensure data accuracy and pipeline reliability?
I implement schema validation rules, data quality checks, edge-case handling (like null or duplicate handling), and automated unit tests to ensure the pipeline runs reliably without failing silently.
Do you provide documentation and post-delivery support?
Yes. Every completed delivery includes clear setup documentation and code comments so you can easily maintain and operate the pipeline going forward.

