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ETL Migration to Databricks: Full migration of legacy Informatica PowerCenter ETL workflows to Azure Databricks and Azure Data Factory. Converted complex Informatica mappings to optimized PySpark transformations. Built parameterized ADF pipelines for automated scheduling, monitoring, and error alerting. Delivered 100% reconciliation validation between legacy and migrated pipeline outputs. Tech: Informatica PowerCenter, Azure Databricks, Azure Data Factory, PySpark, Delta Lake.
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Why AI Automation Fails Without a Real Workflow Strategy
AI automation is powerful, but many businesses fail with it because they start with tools instead of workflows.
They say, "Let’s use ChatGPT, Make, Zapier, or n8n."
But the real question should be:
Where is the business losing time every week?
Most automation problems are not only technical. They are workflow problems.
For example, a company may want to automate sales follow-ups. But before building anything, we need to understand:
Where does the lead come from?
What data needs to be collected?
When should the follow-up happen?
What should AI write?
When should a human review it?
What happens if the API fails?
Where should the final result be saved?
A good automation system is not just a chain of tools.
It needs clear inputs, clean data, fallback logic, error handling, logging, and human approval where needed.
That is the difference between a simple automation and a reliable business system.
My approach is simple:
First, understand the workflow.
Then, remove unnecessary manual steps.
After that, build the automation using tools like n8n, Make, Zapier, OpenAI API, LangChain, or custom APIs.
AI should not replace business logic.
It should make the workflow faster, cleaner, and easier to scale.
#AIAutomation #WorkflowAutomation #BusinessAutomation #AIWorkflowStrategy #n8n #Make #Zapier #OpenAIAPI #LangChain #APIIntegration #HumanInTheLoop #ErrorHandling #ScalableAutomation #AIAgents #OperationsAutomation
Hey Contra community! 👋
I'm Madhavi Akella, a Data & AI Engineer newly joined Contra and excited to connect with potential clients and collaborators.
A little about me:
🏢 Enterprise Data Engineering
5 years at Accenture delivering production ETL pipelines, data warehouse solutions, and large-scale insurance analytics systems using Informatica PowerCenter, SQL Server, and IICS. Received Champion Award and ACE Award for zero-defect delivery and 65%+ pipeline performance improvements.
🤖 Applied AI & GenAI Development
Databricks Certified Generative AI Engineer with 5 live deployed AI applications covering RAG pipelines, NLP, ML forecasting, sentiment analysis, and HR analytics —all independently built and running in production.
What I can help you with:
→ RAG pipelines and LLM applications (LangChain, FAISS, Amazon Bedrock)
→ Cloud data engineering (Azure Databricks, PySpark,Snowflake, Delta Lake, Medallion Architecture)
→ ML models and AI dashboards (scikit-learn, Python, Streamlit)
→ ETL migration and modernization (Informatica to Databricks, ADF orchestration)
Certifications:
✅ Databricks Generative AI Engineer Associate
✅ Microsoft Azure AZ-900
✅ Snowflake Data Warehousing
✅ IBM Data Science
✅ UC Berkeley AI Business Strategies
⏳ AWS AI Practitioner (In Progress)
🌐 Portfolio: madhavi-akella.netlify.app
⬡ GitHub: github.com/madhavi-akella-lab
Based in Cincinnati, Ohio — available for remote projects globally. If you need reliable, documented, production-ready Data & AI engineering work, I'd
love to connect! 🙌
#DataEngineering #GenerativeAI #RAG #LangChain
#Databricks #AWS #Azure #MachineLearning #Python
#Freelance #Contra #OpenToWork
Just shipped Smart Season a full-stack field monitoring system. 🚀
Built this from scratch as a complete agricultural operations platform. Here's what it does and how it works under the hood.
The problem it solves:
Farm operations with multiple field agents have no structured way to track crop progression, catch struggling fields early, or hold agents accountable for updates. Smart Season fixes that.
What I built:
The system gives admins full control to create fields, assign them to agents, and get a real-time view of every field across the entire operation. Agents get a focused interface showing only their assigned fields, where they can update the crop stage and log observations.
The most interesting piece is the computed status engine. Instead of storing a "status" value in the database (which goes stale the moment conditions change), status is derived on every read based on two inputs: the current stage and days since planting. A field still in the Planted stage after 30 days automatically surfaces as At Risk: no cron jobs, no manual updates, no stale data.
Every stage change is captured in an audit log; admins see the full history across all fields, and agents see only their own. This turns the system from a simple tracker into something with real operational accountability.
Tech stack:
Node.js + Express + TypeScript
Prisma ORM + PostgreSQL on Neon
JWT authentication with role-based access control
React 18 + Vite + Tailwind CSS
Deployed on Railway (backend) + Vercel (frontend)
What I learned:
Separating computed values from stored values is one of those decisions that feels like extra work upfront but saves enormous complexity later. Keeping the service layer as the single source of truth for business logic and never letting controllers or repositories make decisions made the codebase genuinely easy to reason about as it grew.
The audit log wasn't in the original requirements. I added it because a monitoring system without history isn't really monitoring; it's just a snapshot. Sometimes the most valuable features are the ones you identify beyond the spec.
GitHub: https://github.com/jeffRnR/smartseason
#fullstack #nodejs #react #typescript #prisma #postgresql
nice work
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