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I will provide expert level solutions for custom data and etl pipelines
About this Gig
Struggling with slow data, broken pipelines, or fragmented storage?
In 2026, data value is defined by speed. I provide high-performance Data Engineering for startups requiring a Modern Data Stack on AWS, BigQuery, or Snowflake.
My "Architect-First" Approach: I don't just write scripts; I design resilient systems. My methodology focuses on:
- Decoupled Storage & Compute: Optimized architectures to prevent cost scaling.
- Idempotent Pipelines: Fault-tolerant systems that restart without data duplication.
- Proven Success: Architected an S3-to-Redshift finance pipeline, reducing latency by 40% and cutting cloud costs by 25% via optimized partitioning and dbt modeling.
What I Offer:
- Automated Pipelines: Seamless extraction from APIs, SQL, or scrapers.
- ETL/ELT: Advanced cleaning using Python (Polars/Pandas) and SQL.
- Orchestration: Industrial-grade scheduling with Apache Airflow DAGs.
- Performance Tuning: Optimization for high-concurrency environments.
Why Me? With a background in IT and Software Engineering, I build production-ready infrastructure. I prioritize security, documentation, and clean handoffs.
Ready to automate? Message me today to build a system that fuels your growth!
FAQ
Do I need to provide my own AWS/Snowflake account?
Yes. To ensure you maintain full ownership of your data and infrastructure, I will build the solution directly within your environment. I can assist with account setup if needed.
Can you handle real-time streaming data or just batch?
I specialize in both. While the standard package covers batch ETL, I can design high-performance streaming pipelines for real-time analytics as a custom requirement
What happens if the API I’m using changes its structure?
I build resilient pipelines with error handling. For long-term peace of mind, I offer maintenance retainers to update your code if external sources change
Is my data secure during the process?
Absolutely. I follow best practices for data privacy, including using environment variables for secrets and never hardcoding sensitive credentials
How do you handle interruptions or failures in the data flow?
I build idempotent pipelines with automated retries and error alerting. Using Airflow DAGs, the system handles interruptions by preserving data integrity and preventing duplicates upon restart, ensuring no data loss during failures
