You will get a production-ready ETL pipeline on AWS/Azure/GCP
Project details
I will build you a fully automated, production-ready ETL/ELT data pipeline on AWS/Azure/GCP. Your data moves from source to destination reliably, on schedule, and without any manual effort on your end.
Whether you need to pull data from APIs, databases, or flat files and load it into Snowflake, Redshift, Azure Synapse, or a data lake, I design scalable and well-monitored pipelines using tools like Apache Airflow, dbt, PySpark, and AWS Glue.
What sets this apart is that I do not just write scripts. I build proper production infrastructure with logging, error alerts, retry logic, and clear documentation so your team can confidently own and maintain it after handover.
Tools and technologies I work with: Python, SQL, PySpark, dbt, Apache Airflow, AWS (S3, Glue, Lambda, Redshift), Azure (Data Factory, Synapse, ADLS), GCP, Snowflake, and Databricks.
This project is a great fit for startups and growing businesses that are tired of manual data exports, broken spreadsheet workflows, or disconnected data systems and want a clean, automated foundation to build on.
Whether you need to pull data from APIs, databases, or flat files and load it into Snowflake, Redshift, Azure Synapse, or a data lake, I design scalable and well-monitored pipelines using tools like Apache Airflow, dbt, PySpark, and AWS Glue.
What sets this apart is that I do not just write scripts. I build proper production infrastructure with logging, error alerts, retry logic, and clear documentation so your team can confidently own and maintain it after handover.
Tools and technologies I work with: Python, SQL, PySpark, dbt, Apache Airflow, AWS (S3, Glue, Lambda, Redshift), Azure (Data Factory, Synapse, ADLS), GCP, Snowflake, and Databricks.
This project is a great fit for startups and growing businesses that are tired of manual data exports, broken spreadsheet workflows, or disconnected data systems and want a clean, automated foundation to build on.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$75
|
Standard
$150
|
Advanced
$275
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Model Documentation | - | ||
Data Source Connectivity | |||
Model Validation/Testing | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $70
Additional Revision
+$25Frequently asked questions
About Hamna
AI Data Engineer | Building Production-ready AI Data Infrastruture
Bahawalpur, Pakistan - 1:59 am local time
𝗖𝗼𝗿𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗖𝗹𝗼𝘂𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
• Modern Data Lakehouses: Designing multi-tier architectures using Databricks, Snowflake, and Apache Iceberg to support both BI and AI workloads.
• High-Scale Pipelines: Building robust ETL/ELT workflows with PySpark, ADF, AWS Glue, and Airflow, incorporating CDC (Change Data Capture) and real-time streaming.
• Data Modeling & Governance: Implementation of Star/Snowflake schemas, SCD Type 2 tracking, and enterprise-grade governance via Unity Catalog or Lake Formation.
• Optimization: Advanced Spark tuning, query optimization, and infrastructure cost-performance balancing.
🤖 AI & LLM Development: I build LLM agents with multi-turn state using LangChain and LangGraph — the kind that run in production on Telegram, Discord, and WhatsApp. For knowledge retrieval I work with RAG pipelines backed by Qdrant and LLaMA-Index, with real semantic search over internal documents. Prompt engineering is a proper workflow here — evaluation frameworks, LangSmith observability, structured output validation. I integrate agentic workflows through API (OpenAI, Claude, Anthropic, Gemini) with full output control.
☁️ Cloud & Infrastructure: I work with the full AWS stack in production — S3, SQS, Lambda, Athena, DynamoDB, EKS, Kinesis, Bedrock, CloudFront, also Docker, Kubernetes (Helm + ArgoCD), and Terraform for reproducible infrastructure as code. Observability built in Datadog APM, OpenTelemetry, Sentry, and vector database management with Qdrant and AWS OpenSearch for semantic search and RAG retrieval.
🧭 When you choose me, you get a clear roadmap:
- Consultation — I start by understanding your request, goals, and constraints before we start
- Honest scoping — based on your needs I give you a real estimate with clear timelines
- Full transparency — Agile workflow, regular meetings, and open communication at every step of the process
- Clean delivery — proper documentation and handoff, so you truly own what I build
- Support from idea to launch — I stay in contact after delivery, and stay involved as long as you needed
📩 Ready to get started? Contact me — I respond fast.
Buzz words: Java JavaScript Python PHP API Web Development Node.js Android API Integration React Android App Development HTML MySQL Mobile App Development Amazon Web Services RESTful API Artificial Intelligence Web Application HTML5 CSS Spring Boot C++ iOS C# Smartphone Machine Learning AI Agent Development AI Development PostgreSQL AI App Development Data Scraping Data Science Automation Deep Learning AI Model Integration TensorFlow Django Natural Language Processing Claude Chatbot Development API Development TypeScript n8n OpenAI API AI Bot Full-Stack Development FastAPI Docker Next.js LLM Prompt Engineering Data Mining Computer Vision SaaS LangChain Software Architecture & Design Data Extraction Database Architecture SQL Lead Generation Vector Database Web Scraping Java Spring Boot Spring Framework Microservices Cloud Architect Terraform Apache Kafka Auth0 OAuth 2.0 JWT SSO Hibernate JPA JUnit Mockito Testcontainers Maven Gradle GitLab CI/CD Jenkins BitBucket Pipelines Stripe API GraphQL gRPC Java 21 AWS Lambda AWS ECS AWS API Gateway Web Platform Backend Architecture AI Integration
Steps for completing your project
After purchasing the project, send requirements so Hamna can start the project.
Delivery time starts when Hamna receives requirements from you.
Hamna works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Architecture Design
Analyze your data sources, define schema, and design the pipeline architecture diagram for your approval before building.
Pipeline Development & Testing
Build ETL scripts, transformations, and Airflow DAGs. Run end-to-end tests with sample data to validate accuracy.