Wavicle Data Solutions

Lead Data Engineer

LinkedIn

LinkedIn Learning Instructor

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2024 
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

73% of data pipeline failures happen because of dependency issues, not bad code.
Cron jobs don't care if yesterday's data never arrived. They'll happily run on empty tables and produce garbage.

Airflow? It checks, retries, alerts, and refuses to cascade your failure downstream.

→ Beyond scheduling Airflow gives an important feature of 𝗗𝗔𝗚𝘀(𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀), that helps to monitor, retry and gives flexibility to organize pipelines with minimum/less code.

🤔 Data Engineers often misinterpret Airflow just as an orchestration tool.

Apache Airflow is an open-source platform designed to programmatically author, schedule, retry and monitor workflows. 

🎼Think of it as the "conductor of an orchestra", where each musician (your data tasks, like data extraction, transformation, loading, or model training) knows when to play, what to play, and what happens if they make a mistake.

Most data engineers automate the wrong things at the wrong time.

Here's the framework I use after 8 years of building production systems:
✅ AUTOMATE WHEN: 
→ Task runs daily/weekly 
→ Human errors cause outages 
→ Work blocks other priorities
→ Team growth = more manual work
Examples: Reports, schema checks, alerts

❌ DON'T AUTOMATE WHEN: 
→ Task happens quarterly 
→ Requirements change weekly 
→ Process isn't understood yet 
→ Manual steps reveal insights

My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it.
Automate what matters, when it matters—not everything!

Airflow isn’t just about running tasks.

🕑 It’s about building trustworthy, scalable, and observable pipelines that run reliably at 2am — even when you’re not watching.

Airflow gives structure to your chaos.
DAGs give clarity to your pipelines. 
That’s why DEs love them ❤️

Real Example: 
Instead of: 
❌ Cron jobs that fail silently 
❌ Manual dependency management
❌ No visibility into failures
You get: 
✅ Visual workflow monitoring 
✅ Automatic failure notifications
✅ Smart task scheduling 
✅ Easy debugging & restarting

Here's an amazing Airflow cheatsheet with some fundamentals concepts and interview questions to prepare for data engineers. 👇

Carousel Credits -  Futurense.com

Willing to learn more, here's the link to Astronomer free learning path - https://lnkd.in/g8-EWBda

Also, level up with Marc Lamberti's Udemy course on Airflow 3 - https://lnkd.in/gwArwaME

Airflow

•Context-Aware QA Chatbot: slashing client data retrieval time by 60% using 
Python, Langchain, and IBM Granite 3.2 LLM, integrating Elasticsearch for 
precise, data-driven answers from Proprietary datasets.
•Enterprise Scalability: End-to-End delivery (development through deployment) of 
scalable Generative AI and LLM solutions, customized to client workflows, 
demonstrably enhancing system performance and operational agility.
•Engineered autonomous AI agents using CrewAI and Llama 3.3, achieving a 40% improvement in decision making accuracy and response timeliness. Architected an intelligent framework that empowered the agent to dynamically select the optimal tool RAG for contextual data, a math tool for calculations, or real-time web search based on the user’s query. This multi-tool capability enabled robust, multi-step reasoning, allowing the agent to overcome base LLM limitations and deliver highly accurate, context-aware solutions.

AI Engineer

Thomson Reuters

•Developed all AI-driven Solutions in AWS cloud(Bedrock).
•GenAI Chatbot Development: Built and deployed AWS cloud-native (SageMaker, 
Bedrock) chatbots using Python, Langchain, and OpenAI (GPT-4 Turbo/GPT-4o), 
enhancing customer NPS score from 8 to 9 via transformer architectures and 
Advanced RAG.
•Enterprise Advanced AI Solutions: Engineered custom prompt workflows and 
Weaviate vector DB integration for optimized query processing, improving 
response accuracy by 25%+ using company-specific data.
•Customer feedback Insight Extraction: Leveraged NLP (Nltk) and LLMs to 
identify actionable pain points from unstructured text, driving data-backed 
product decisions.Leveraging this cutting-edge techniques to unlock hidden 
patterns and trends

Data Scientist

Convertcart

•Driven a 2% increase in overall sales and revenue by developing predictive 
models for customer engagement.
•Acquired and integrated datasets from multiple sources, performing rigorous 
Data Preprocessing to ensure data quality and accuracy.
•Conducted comprehensive Exploratory Data Analysis (EDA), Statistical Analysis, 
and Data Visualization to uncover key insights into customer behavior.
•Applied advanced Feature Selection techniques and Regression Modeling to 
build accurate predictive models for customer engagement scores.
•Implemented Unsupervised Learning (Customer Segmentation) to identify 
distinct customer groups, enabling targeted engagement strategies and 
optimized resource allocation.

Analyttica Datalab Inc.

•Developed and Dockerized deployment of an NLP solution using Python (NLTK, 
scikit-learn, TensorFlow) to automatically prioritize support tickets, significantly 
enhancing operational efficiency through Natural Language Processing.
•Delivered multi-million dollar cost savings and reduced ticket resolution time 
by 100 hours by accurately classifying priority issues using custom-built MLP 
models, demonstrating strong Predictive Modeling capabilities.
•Engineered robust end-to-end ML pipelines involving Text Preprocessing, 
Feature Engineering (word2vec, TF-IDF), and handling Imbalanced Data for 
Machine Learning model development.
•Implemented, evaluated (Model Evaluation), and optimized diverse ML algorithms 
(Logistic Regression, Decision Trees, Random Forest, Gradient Boosting) via 
Hyperparameter Tuning using Python (NumPy, pandas, scikit-learn).
•Managed the full MLOps cycle: instituted monthly model retraining, conducted 
A/B Testing for performance validation, and ensured continuous model 
monitoring in production using Deep Learning (TensorFlow) techniques.
•Proficiently presented technical solutions and impact analysis to stakeholder, 
translating complex Machine Learning technquies and Intelligent automation 
into business value (Stakeholder Communication)

AI Engineer @ IBM | 4 Yrs Exp. in ML, DL, DS, NLP, GenAI & Agentic AI | Ex-Thomson Reuters | VIT Alumni

Senior AI Engineer at IBM with a 4-years of track record of developing and deploying high-impact AI solutions. I thrive at the intersection of Machine Learning, Deep Learning, NLP, and the forefront of Generative AI. My toolkit includes Python, TensorFlow, Keras, and an advanced command of the modern NLP/GenAI ecosystem: LLMs, LangChain, RAG, prompt engineering, sophisticated fine-tuning, and vector databases,Tools,Agents.My analytical prowess is powered by Python (NumPy, Pandas, Matplotlib/Seaborn/Plotly), SQL, and robust data management practices. I am deeply passionate about pushing the boundaries of AI through continuous learning and creating innovative solutions.

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Pooja Jain

Achutha Subhash

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The Battle

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Achutha Subhash

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Pooja Jain's LinkedIn Performance

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