Tim Chowdary - Professional Portfolio

About Me

As an RSA Delivery Manager – AI Automations at Databricks India, I help customers accelerate innovation through Data + AI transformation powered by Delta Lake and modern Data Intelligence architectures. I have led the design and delivery of enterprise-scale, multi-agent AI frameworks, including for one of Europe's largest electrical organizations—driving significant improvements in efficiency, accuracy, and time-to-value. I have also architected agentic recruiting automation platforms and bring a proven track record of building, mentoring, and leading high-performing Data & AI teams to deliver business-critical outcomes.

I bring deep, hands-on expertise in cloud-native development across Databricks, AWS, and multi-cloud ecosystems, with a strong emphasis on scalable data architectures, reliability, and access controls. Known for strong customer obsession and a product-first mindset, I excel at translating complex business needs into actionable roadmaps and leading distributed teams to deliver measurable impact across analytics, automation, and AI-driven product initiatives.

Beyond my professional role, I actively write articles and create videos on leadership, Generative AI, and software development. I enjoy "vibe coding" and building mini-projects that push me technically and creatively. Passionate about teaching and mentoring, I regularly visit engineering colleges to share industry trends, guide students on career readiness, and help them prepare for real-world technology roles.

I use diverse thinking styles like Strategic (long-term vision), Critical (questioning assumptions), Systems (seeing interconnections), Expert (deep knowledge), and Creative/Explorer (idea generation) to adapt to challenges, viewing thinking as a versatile skill set for varied situations, not just one comfortable mode. I combine analytical rigor with big-picture vision and human-centric approaches, often incorporating elements of servant, transformational, and democratic styles.

Key Thinking Styles & Approaches

🎯

Strategic Thinking

Focuses on the future, asking "what if," and anticipating changes for long-term success.

🔍

Critical Thinking

Involves questioning assumptions, evaluating information, and reframing problems for innovation.

🔗

Systems Thinking

Understands how different parts of an organization interact and how changes in one area affect others.

🎓

Expert Thinking

Leverages deep knowledge and experience to solve problems quickly.

💡

Creative/Explorer Thinking

Generates novel ideas and possibilities.

📊

Analytical Thinking

Uses data and logic for organization and repeatable systems.

🤝

Servant Leadership Mindset

Prioritizes employee fulfillment, well-being, and empowerment to boost effectiveness.

✨

Transformational Leadership

Inspires and motivates positive change by focusing on vision and people.

💬

Democratic/Inquiry-Based Thinking

Actively solicits input and feedback from the team for shared decision-making.

How I Apply Them

🏃

Cognitive Agility

I act as "cognitive athletes," switching thinking styles as needed (e.g., analytical for efficiency, creative for new ideas).

🧠

Self-Awareness

Understanding my own natural thinking preferences helps me recognize when I need other styles or perspectives.

❓

Focus on "Why"

I don't just accept "how" things are done but question the underlying reasons and assumptions.

🌍

Holistic View

I see the bigger picture while also diving deep into details, understanding interconnectedness.

Databricks

RSA Delivery Manager – AI Automations

Bangalore | Jan 2024 – Present

I lead strategic delivery and thought leadership initiatives focused on enterprise-scale migrations, AI-driven code modernization, and metadata-centric automation platforms.

Shaped product and delivery direction for large-scale enterprise migrations by aligning engineering capabilities with customer requirements and long-term platform vision.
Owned requirements for evolving an AI-powered metadata catalog covering code artifacts, Spark lineage, and migration states—enabling end-to-end traceability and audit readiness.
Designed governance frameworks for automated code conversion, including access controls, sensitive SQL pattern detection, data classification, and rule-based validation.
Translated customer objectives into operational requirements for centralized metadata persistence, consistent LLM behavior, and reproducible transformation outcomes using Model Context Protocol (MCP).
Evangelized product vision across engineering, field teams, and customers—balancing near-term delivery with long-term scalability, reliability, and governance.
Established standardized practices for data reliability, code validation workflows, quality gates, lineage capture, and automated documentation—improving delivery consistency by 40%.
Partnered closely with Engineering and Product teams to influence platform features, resolve data pipeline reliability gaps, and manage high-risk customer escalations.
Built customer-facing automation assets, scoping tools, and generative AI assistants that reduced manual engineering effort and accelerated customer adoption.

Amazon

Data Engineering Manager

Bangalore | Oct 2015 – Dec 2023

Led data engineering initiatives supporting large-scale analytics and decision-making platforms.

Implemented machine learning–based seller forecasting solutions using Random Forest models to improve planning and operational efficiency.
Designed and delivered enterprise BI pipelines using Amazon Redshift, EMR, and Step Functions, significantly enhancing reporting reliability and business intelligence capabilities.
Collaborated with cross-functional teams to scale data platforms, improve data quality, and support high-volume analytical workloads.

Earlier Career Experience

TCS

Senior Technical Lead

Bangalore | Dec 2011 – Sep 2015

Led enterprise data and application initiatives, mentoring teams and delivering large-scale systems in customer-facing environments.

HCL

Technical Lead

Bangalore & UK | Dec 2007 – Dec 2011

Drove delivery of distributed systems across onshore and offshore teams, supporting global customers.

iGATE

Software Engineer

Bangalore | Sep 2005 – Dec 2007

Built and supported enterprise software solutions, laying a strong foundation in software engineering and system design.

Prompt Engineering with ChatGPT

LinkedIn | July 2025

Demonstrates advanced capability in designing effective prompts to drive accurate, consistent, and context-aware AI outputs. Focused on real-world application of prompt patterns for productivity, reasoning, and automation.

Critical Thinking for Better Judgment and Decision Making

LinkedIn | March 2024

Strengthens structured thinking, problem framing, and decision-making under uncertainty. Emphasizes bias awareness, analytical rigor, and outcome-driven judgment in leadership and technical contexts.

Prompt Engineering – How to Talk to AI

LinkedIn | March 2024

Builds foundational skills in interacting with large language models using clear intent, constraints, and context. Enables effective human–AI collaboration for problem solving and content generation.

Academy Accreditation – Generative AI Fundamentals

Databricks | Feb 2024

Covers core concepts of Generative AI, including LLM architectures, use cases, and enterprise adoption patterns. Focused on responsible AI, scalability, and real-world implementation on the Databricks platform.

Academy Accreditation – Databricks Lakehouse Fundamentals

Databricks | Dec 2024

Validates strong understanding of Lakehouse architecture, Delta Lake, and unified analytics workflows. Emphasizes data reliability, governance, and performance optimization at scale.

AWS Certified Solutions Architect – Associate (CSA)

Amazon Web Services | Jan 2024

Demonstrates expertise in designing secure, scalable, and cost-optimized cloud architectures on AWS. Covers core services, best practices, and architectural decision-making for production workloads.

Thought Leadership

DNA of High-Performing Teams

High-performing teams are not built by chance. They exhibit a distinct DNA—a set of shared values, behaviors, and practices that consistently enable them to rise above challenges and deliver exceptional outcomes. The following traits form the core of that DNA:

Trusted and Authentic Leadership

Leaders earn trust through transparency, integrity, and consistency between words and actions. They create psychological safety, empowering teams to speak up, challenge ideas, and take ownership without fear.

Not Afraid of Failure

High-performing teams view failure as a learning signal, not a setback. This mindset encourages experimentation, innovation, and informed risk-taking.

Fail Fast, Learn Faster

They identify what doesn't work early, minimize the cost of failure, and rapidly course-correct. Speed of learning becomes a competitive advantage.

DNA of High Performing Teams Infographic

Resilient and Adaptable

When faced with setbacks, these teams recover quickly and refocus on outcomes. Resilience enables them to sustain momentum even in uncertain or high-pressure environments.

Compelling Goals with Clear Strategy

They rally around ambitious, well-defined goals supported by coherent strategies and execution plans. Every team member understands the "why," the "what," and the "how."

Interdependent with Respect for Diverse Viewpoints

Success is driven by collaboration, not heroics. Teams value diverse perspectives, actively listen, and leverage complementary strengths to make better decisions.

Commitment to Continuous Learning

High-performing teams invest in constantly upgrading their skills and knowledge. They reflect, seek feedback, and evolve—individually and collectively—to stay relevant and effective.

Team Management Strategies for Building High-Performing Teams

High-performing teams are not built by chance. They are the outcome of clear principles, intentional leadership, and disciplined execution. Over time, I've learned that sustainable performance emerges when trust, clarity, and ownership come before speed and short-term wins.

This one-pager outlines the operating principles, people KPIs, and mental models that consistently drive strong, resilient, and motivated teams.

Operating Principles (Non-Negotiables)

These principles act as the north star for every decision, trade-off, and execution path.

1

Customer Trust > Profit

Trust is the strongest growth multiplier. When teams prioritize doing the right thing for customers—consistently and transparently—profit becomes a natural by-product.

Profit follows trust, not the other way around.

2

Efficiency > Speed

Moving fast is meaningless if you move in the wrong direction. High-performing teams focus on doing things right the first time, even if progress is incremental.

Sustainable velocity beats short bursts of speed.

3

Long-Term Value > Short-Term Gains

Never compromise long-term credibility, architecture, or people culture for quick wins. Teams that think long-term build systems—and reputations—that last.

4

Quality > Quantity

Delivering many half-baked solutions erodes trust internally and externally. One well-designed, fully functional solution creates more impact than ten unfinished ideas.

If you were to build an aircraft, you should be the first person willing to take a ride in it.

Ownership begins with accountability.

People-Centric KPIs (The Real Performance Indicators)

Traditional metrics measure output. High-performing teams measure engagement, ownership, and inspiration.

📢

Keep People Informed

Share goals, purpose, and strategy openly
Provide context behind decisions
Reduce ambiguity to increase confidence

Clarity eliminates friction.

🤝

Keep People Involved

Actively listen
Encourage dissent and diverse perspectives
Close the loop by acting on feedback

Involvement turns contributors into collaborators.

💡

Keep People Interested

Give teams ownership, not tasks
Align work with impact and growth
Allow autonomy with responsibility

People stay engaged when they feel they matter.

✨

Keep People Inspired

Recognize effort, not just outcomes
Reward behaviors that reflect values
Celebrate progress, learning, and resilience

Motivation scales when recognition is intentional.

Frameworks & Mental Models for Continuous Improvement

SCAMPER: A Practical Thinking Framework for Teams

A powerful model to challenge assumptions and improve execution:

S

Substitute – Can a simpler or more cost-effective approach replace the current one?

C

Combine – What ideas, tools, or workflows can be merged for better outcomes?

A

Adapt – How should we adjust to changing business or customer needs mid-execution?

M

Magnify / Minimize – What should we scale up or cut down based on real impact?

P

Put to Another Use – How can we reuse existing assets or capabilities?

E

Eliminate – What adds complexity without value?

R

Reverse / Rearrange – What happens if we invert the problem or fail fast?

Great teams don't just execute—they continuously rethink how they execute.

Leadership Philosophy: The Multiplier Effect

A leader's role is not to be the smartest person in the room—but to create an environment where smart people do their best work.

High-performing teams thrive when leaders:

Build psychological safety
Encourage ownership over compliance
Balance empathy with high standards
Use principles as guardrails, not constraints

Final Thought

High-performance is not about pressure—it's about clarity, trust, and purpose. When teams are aligned on values, empowered with ownership, and supported by the right frameworks, exceptional outcomes follow naturally.

Strong teams build great products. Great teams build great companies.

Frameworks and Mental Models

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Featured Projects

🚀 AI-Powered Recruitment

Elite Hire - AI Hiring Workflow

Transforming recruitment through intelligent automation

⚡

80%

Time Saved

📊

95%

Accuracy Rate

👥

1000+

Resumes Processed

🎯

10x

Faster Hiring

🎯

Project Goals

1
Automate Resume Screening
Reduce manual effort in reviewing hundreds of resumes by implementing AI-powered intelligent screening and candidate matching.
2
Streamline Interview Scheduling
Create automated workflows for scheduling interviews, managing calendar conflicts, and sending notifications to all stakeholders.
3
Enhance Candidate Experience
Provide timely communication, personalized responses, and transparent feedback throughout the hiring process.
4
Scale Recruitment Operations
Build a system capable of handling high-volume hiring while maintaining quality and compliance standards.

⚠️

Key Challenges

1

Manual Resume Screening Bottleneck

Recruiters spent 60-70% of their time manually reviewing resumes, leading to slow response times and missed qualified candidates.

2

Inconsistent Evaluation Criteria

Different reviewers applied varying standards, resulting in subjective assessments and potential bias in candidate selection.

3

Complex Scheduling Coordination

Coordinating interview schedules across multiple time zones and stakeholders created significant administrative overhead.

4

Poor Candidate Communication

Delayed responses and generic communications led to negative candidate experiences and damaged employer brand.

💡

Solution Architecture

Built an end-to-end AI-powered recruitment platform leveraging Databricks Foundation Models and modern data engineering practices.

1

Intelligent Resume Parsing

Leveraged Claude 3.7 to extract structured data from resumes including skills, experience, education, and achievements with 95% accuracy.

Claude 3.7 Python PDF Parsing

↓

2

AI-Driven Candidate Matching

Implemented semantic matching algorithms to score candidates against job requirements, ranking top fits automatically.

Embeddings Semantic Search Databricks

↓

3

Automated Scheduling Engine

Built intelligent calendar integration that finds optimal interview slots considering all participants' availability and preferences.

Calendar APIs Workflows Notifications

↓

4

Personalized Communication

Generated context-aware emails and feedback using AI, ensuring timely and personalized communication at every stage.

Email Automation Templates AI Generation

📈

Business Impact

80%

↓

Reduced Screening Time

Automated resume analysis cut screening time from 10 hours to 2 hours per 100 candidates, freeing recruiters to focus on high-value interactions.

10x

↑

Faster Time-to-Hire

Average time from application to offer reduced from 21 days to 7 days, improving competitive advantage in talent acquisition.

95%

✓

Improved Candidate Satisfaction

Candidate NPS scores increased from 45 to 85 due to faster responses, clear communication, and transparent feedback.

40%

↓

Cost Savings

Reduced hiring costs by 40% through automation, better candidate quality, and decreased time-to-fill for critical positions.

Technology Stack

🧠 Claude 3.7 Sonnet

⚡ Databricks

🐍 Python

🔄 Databricks Workflows

📧 Email APIs

📅 Calendar Integration

🗄️ Delta Lake

🔐 OAuth 2.0

View Full Project on GitHub →

Scopilot

Scopilot in an LLM based Scoping Agent that helps in the initial scoping of any migration project. Assessing code complexity, data volume, and other factors to help in the initial scoping of any migration project.

Python Delta Tables Databricks Workflows

View Project →

🌍 Global Data Intelligence Platform

Amazon Seller Intelligence

Empowering millions of sellers with real-time insights and intelligent forecasting

📉

30%

Cost Reduction

📦

50%

Less Out-of-Stock

🌐

1M+

Global Sellers

🎯

12mo

Forecast Range

🎯

Project Vision

1
Global Data Lake Architecture
Build a comprehensive, scalable data lake capable of processing and analyzing data from millions of sellers across global marketplaces while maintaining regional compliance and data sovereignty.
2
Regional Processing, Global Visualization
Implement distributed data processing with regional caching for faster local operations while enabling unified global reporting and insights across all seller accounts.
3
Intelligent Recommendations Engine
Transform the platform from passive reporting to active intelligence—providing seasonal demand patterns, inventory optimization recommendations, and market opportunity alerts to sellers.
4
Predictive Analytics & Forecasting
Deploy machine learning models for demand forecasting covering seasonal trends (education supplies, thermal wear, holidays) to help sellers optimize inventory 6-12 months in advance.

⚠️

Business Challenges

1

Massive Resource Wastage

30% of compute resources sat idle daily due to fixed-size infrastructure designed for peak loads. This resulted in significant cost inefficiency and inability to scale elastically with fluctuating seller activity across time zones.

2

Ineffective OLAP Reporting

Traditional OLAP reporting systems failed to provide actionable insights for regional inventory planning. Sellers couldn't identify local demand patterns or make data-driven decisions about stock allocation and distribution.

3

Manual Hot ASIN Identification

Identifying high-demand products (Hot ASINs) was a manual, human-driven process. This led to missed opportunities in demand forecasting, resulting in stockouts during peak periods and overstocking during off-seasons.

4

Reactive vs. Proactive Intelligence

Sellers received historical reports but lacked predictive insights for future planning. This reactive approach caused inventory imbalances, lost sales opportunities, and poor cash flow management.

💡

Solution Architecture

Re-engineered the platform with serverless, event-driven architecture leveraging AWS managed services for elastic scalability and cost optimization.

1

Event-Driven Data Ingestion

Implemented S3-triggered Lambda functions to automatically process incoming seller and order data in real-time, eliminating batch processing delays and enabling continuous data flow across regions.

S3 Event Notifications AWS Lambda Kinesis Streams

↓

2

Elastic ETL Processing

Deployed AWS Glue with auto-scaling DPU (Data Processing Units) that dynamically adjust based on workload. This eliminated idle resources during off-peak hours and scaled up during high-traffic periods.

AWS Glue Auto Scaling PySpark

↓

3

Regional Data Caching & Processing

Built regional data lakes with S3 cross-region replication for fast local access while maintaining global data consistency. Enabled sellers to access insights with minimal latency regardless of geographic location.

S3 Replication CloudFront Regional Caching

↓

4

ML-Powered Demand Forecasting

Developed Random Forest models to identify seasonal patterns and predict demand 6-12 months ahead. Automated Hot ASIN detection and generated personalized inventory recommendations for each seller.

SageMaker Random Forest Time Series Analysis

↓

5

Intelligent Alerting & Visualization

Created real-time email alerts via SNS for critical inventory events and market opportunities. Built interactive dashboards on Seller Central for both regional account views and global insights.

Amazon SNS QuickSight CloudWatch

📈

Business Impact

30%

↓

Reduced Compute Costs

Auto-scaling Glue jobs eliminated 30% of idle compute resources by dynamically adjusting capacity. Annual infrastructure savings exceeded $2M while improving processing speed by 40%.

50%

↓

Fewer Stockouts

ML-powered demand forecasting reduced out-of-stock incidents by more than 50%, preventing lost sales opportunities and improving customer satisfaction scores by 25%.

30%

↓

Optimized Inventory Levels

Seasonal demand predictions and automated Hot ASIN alerts helped sellers reduce stock pile-up by 30%, improving cash flow and warehouse utilization across the seller network.

12mo

📅

Long-Term Forecasting

Extended forecast horizon from 3 months to 12 months enabled strategic planning for major seasons (back-to-school, holidays, winter), resulting in 35% higher profit margins for participating sellers.

1M+

👥

Scaled to Millions of Sellers

Platform successfully scaled to serve over 1 million global sellers with consistent sub-second query response times and 99.99% uptime across all regions.

Real-Time

⚡

Proactive Intelligence

Automated email alerts and real-time dashboards transformed seller behavior from reactive to proactive, with 80% of sellers taking action on recommendations within 48 hours.

Technology Stack

📦 AWS S3 (Data Lake)

🔄 AWS Glue (ETL)

⚡ AWS Lambda

🌊 Amazon Kinesis

🤖 Amazon SageMaker

🐍 Python & PySpark

📊 Amazon QuickSight

🔔 Amazon SNS

🌐 Amazon CloudFront

📈 CloudWatch

🔍 Amazon Redshift

🧮 Random Forest ML

This project transformed how millions of Amazon sellers manage inventory, forecast demand, and capitalize on market opportunities.

Skills & Technologies

Data Engineering

Apache Spark
Databricks
Snowflake
Data Warehousing

Cloud Platforms

AWS
Azure
Google Cloud
Cloud Architecture

Programming

Python
SQL
Shell Scripting
JavaScript

AI/ML

Machine Learning
LLMs & Gen AI
MLOps
Model Deployment

Gallery

Annual Planning Summit - October 2025

Tech Summit 2024

Get In Touch

I'm always interested in hearing about new opportunities and collaborations.

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