You will get ETL Pipelines (Python / Airflow)
Project details
I build reliable, scalable ETL pipelines using Python and Apache Airflow, designed for production workloads.
Whether you’re facing slow pipelines, data failures, or need a new ETL system, I’ll help you design, optimize, or fix it with clean, maintainable code.
I focus on performance, reliability, and observability, not just “getting data from A to B”.
Use Cases:
• Analytics pipelines
• Data warehouse ingestion
• Reporting systems
• AI / ML data preparation
• SaaS product data flows
Why Work With Me:
• 7+ years of data & backend engineering experience
• Built and optimized production ETL pipelines
• Strong focus on performance & reliability
• Clean, maintainable, well-documented code
Whether you’re facing slow pipelines, data failures, or need a new ETL system, I’ll help you design, optimize, or fix it with clean, maintainable code.
I focus on performance, reliability, and observability, not just “getting data from A to B”.
Use Cases:
• Analytics pipelines
• Data warehouse ingestion
• Reporting systems
• AI / ML data preparation
• SaaS product data flows
Why Work With Me:
• 7+ years of data & backend engineering experience
• Built and optimized production ETL pipelines
• Strong focus on performance & reliability
• Clean, maintainable, well-documented code
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$180
|
Standard
$500
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$150Frequently asked questions
About Dawood
Data Engineer | Snowflake, Airflow, ETL, Python, AWS, PostgreSQL
Lahore, Pakistan - 6:36 am local time
I help companies build fast, reliable, production-ready data pipelines that power analytics, AI systems, and high-volume applications.
If your data workflows are slow, fragile, or constantly breaking, I design systems that are observable, cost-efficient, and built to scale — not one-off scripts.
📈 Proven Results
Clients typically see:
- 50–80% faster ETL runtimes
- Near-zero pipeline failures after stabilization
- 30–40% lower cloud compute costs
- 100% SLA adherence for analytics & data products
I’ve built systems for asset management, alternative data platforms, fintech, ecommerce, and IoT-driven applications.
🔧 What I Build
ETL & Data Pipelines:
- Apache Airflow DAGs (modular, idempotent, SLA-driven)
- High-performance async & multiprocessing ETL
- Pipeline optimization, retries, monitoring & alerting
- Batch and near-real-time workflows
Databases & Data Warehousing:
- Snowflake (micro-partitioning, clustering, cost optimization)
- PostgreSQL (index tuning, query optimization at scale)
- AWS RDS, external tables, multi-schema warehouse design
Cloud & Infrastructure:
- AWS ECS, S3, Lambda
- Docker-based, containerized data workflows
- CI/CD pipelines for safe, repeatable deployments
Data Modeling & Analytics:
- Star & Snowflake schemas
- Analytics-ready warehouse design
- Batch & streaming data architectures
✅ Why Clients Hire Me
- I build production systems, not fragile scripts
- I design with SLAs, observability, and cost control in mind
- I integrate directly with your stack — no glue tools or wrappers
- I leave behind clean, documented code your team can own
💼 Ideal Projects
- Replacing slow or unreliable ETL pipelines
- Building Airflow-based data platforms from scratch
- Scaling Snowflake or PostgreSQL for analytics
- Migrating ad-hoc scripts into production systems
- Designing data foundations for AI / ML workloads
If you want reliable data delivery instead of constant firefighting, let’s build it right.
📩 Message me to discuss your ETL or data engineering project.
Steps for completing your project
After purchasing the project, send requirements so Dawood can start the project.
Delivery time starts when Dawood receives requirements from you.
Dawood works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Data Review
Review data sources and destinations. Understand current pipeline (if any). Confirm schedule, volume, and business goals.
Pipeline Design
Design ETL flow and transformations. Define Airflow DAG structure. Choose error handling and retry strategy.