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Drivyesh Modi's avatar
Drivyesh Modi
India
Growth is the only constant to achieve Perfection
Growth is the only constant to achieve Perfection
Cover image for Technical Mock interviews and coaching for big firms
Technical Mock interviews and coaching for big firms
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25
Cover image for Java Project and Bug Fixing
Java Project and Bug Fixing
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21
Cover image for Java Application Project
Java Application Project
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25
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Kafka(3)
Allen Tang's avatar
Allen Tang
proMalden, USA
Software engineer - ex-Spotify, ex-Wayfair
    $5k+
    Earned
    1x
    Hired
    5.0
    Rating
    3
    Followers
Software engineer - ex-Spotify, ex-Wayfair
Cover image for During my five years at
During my five years at Wayfair (2015–2020), I spent my first two years building internal tools within Warehouse Management Systems. As the demands of our data workflows evolved, my work naturally led me to transition our legacy, cron-based pipelines into event-driven architecture. To overcome key limitations in the upstream open-source code, I researched, modified, and provisioned Wayfair’s earliest Kafka Connect clusters. To scale this impact, I founded a new Infrastructure team dedicated to supporting event-driven systems company-wide, serving as an internal consultant to help engineering teams migrate from database polling to real-time stream processing.
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Cover image for I was lucky to have
I was lucky to have Jay Sackett of Jay Sackett & Associates reach out to me about refreshing gotūtile, his Chicago-area stone and tile site. His site was built on Weebly and hosted with Network Solutions, and it was showing its age, so we moved it to a modern setup and gave it a fresh look. Before: Weebly site builder on Network Solutions shared hosting. After: a lightweight custom site (HTML, CSS and a little JavaScript) hosted on Cloudflare Pages, with Cloudflare DNS and automatic SSL. Along the way I pulled all 17 pages and 800+ product images over from Weebly and gave the site a clean, showroom-style design. It now has easy dropdown navigation, swatch galleries you can click to enlarge, and works well on phones. If you’re in the Chicago area and looking for limestone, marble, slate, mosaics or tile, check out Jay’s work at gotutile.com (http://gotutile.com). He’s been helping people find the right stone for over 30 years.
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Cover image for During my time at Spotify
During my time at Spotify (2020–2024), I built custom Backstage plugins across several engineering teams within Core Infra. My contributions include: - Reliability: Built a Service Level Platform to enable teams to define SLIs/SLOs, visualize time-series metrics against SLO targets using RechartsJS, and access executive-level summary stats across all services. - Observability: Designed and built the Heroic Migration UI, a tool created to track a company-wide metrics migration effort from our in-house time-series database (HeroicDB) to our Prometheus-based databases. - Storage: Supported a high-priority (P0) initiative by building the GCS Retention Backstage plugin. Partnered with backend storage engineers to design UI workflows that simplified Google Cloud Storage bucket labeling and tracked compliance across the company. - Fleetshift: Provided frontend consulting (architectural design, mob programming, implementation, and handoff) to build the UI for Fleetshift, an internal tool used for large-scale automated code changes. 
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Cover image for My first full-time position out
My first full-time position out of college was at Cambridge Systematics, a transportation consulting company in Boston, from 2013 to 2015. While I was there, I supported development of NYC MTA’s Bus Time app, a real-time bus tracking app on web and mobile. It was a splendid way to kick off my software engineering career, gaining exposure to mapping libraries like LeafletJS and Mapbox. You can check out the app here: https://bustime.mta.info/ and the open source repo here: https://github.com/camsys/onebusaway-nyc
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Kafka(1)
Aishwary Dhare's avatar
Aishwary Dhare
proBengaluru, India
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
    5.0
    Rating
    8
    Followers
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
Cover image for Global FinTech Development for MishiPay
Global FinTech Development for MishiPay
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14
Cover image for High-Performance Cryptocurrency Trading Engine Development
High-Performance Cryptocurrency Trading Engine Development
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12
Cover image for Migration of ETL Pipelines to Apache Airflow
Migration of ETL Pipelines to Apache Airflow
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18
Cover image for Development of PayLater & PayLater Business Apps
Development of PayLater & PayLater Business Apps
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24
Kafka(2)
Jason Fricano's avatar
Jason Fricano
proSacramento, USA
Custom software, business tools & practical AI workflows
New to Contra
Custom software, business tools & practical AI workflows
Cover image for StreamOtter — Live Browser State Toolkit
StreamOtter — Live Browser State Toolkit
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Cover image for Orca Solutions — Studio & Agentic Operations
Orca Solutions — Studio & Agentic Operations
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Cover image for Personal Treasury — Finance App
Personal Treasury — Finance App
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4
Cover image for Jason for You — AI & Technology Consulting
Jason for You — AI & Technology Consulting
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3
Kafka(1)
Sreeram Melarkode's avatar
Sreeram Melarkode
Thane West, India
Wordsmith for hire: Writing, research, consulting
    5.0
    Rating
    7
    Followers
Wordsmith for hire: Writing, research, consulting
Cover image for Near Real Time Data WareHouse
Near Real Time Data WareHouse
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21
Cover image for Solution Consulting
Solution Consulting
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Mobile Device Policy
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25
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Kafka(1)
Soumabha Roy Chaudhuri's avatar
Soumabha Roy Chaudhuri
Bengaluru, India
Fractional CTO | Software Architect | SaaS | AI Agents
Fractional CTO | Software Architect | SaaS | AI Agents
Cover image for 10x throughput · Kafka ·
10x throughput · Kafka · 13 social channels · built for viral-scale message bursts Redesigned an enterprise social platform's data layer into an event-driven architecture on Apache Kafka, ingesting and analysing real-time social data from 13 channels. Sized for viral-scale bursts of 250–1,000 messages/minute per customer, with hybrid delivery paths: streaming (~15–20s average latency) and polling (~2 min), with a worst-case 5-minute latency ceiling. Also set architecture standards across application, data, and AI, and rebuilt the React/TypeScript frontend for multilingual workspaces with automated WCAG 2.2 / ADA Section 508 accessibility checks. End-to-end throughput improved 10x. Confidential client engagement.
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Cover image for Cold start: 17 min →
Cold start: 17 min → under 3 min. Shard loading: 16 min → 50 sec. Serving cost: ~1/6 of a managed SageMaker equivalent. Performance-engineered a LLaVA vision-language deployment on AWS as part of a specialised strike team, with the full write-up published on LinkedIn (linked under Completed Work). Reduced end-to-end cold start from ~17 minutes to under 3 (66%) via custom Deep Learning AMIs (−32% immediately), ephemeral-storage model caching with parallel aria2c downloads (checkpoint loading 16 min → 50 sec), and 4-bit NF4 quantization (shard loading 41s → 18s). Along the way, diagnosed a storage-path bug where checkpoint shards kept re-downloading despite being baked into the AMI.
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Cover image for Cut shipping time 40% with
Cut shipping time 40% with a production multi-agent system built around governance, not demos. Confidential client engagement. Designed and deployed a multi-agent system covering the full product lifecycle: PRD generation → system design → multi-repo execution → code review → compliance/security checks → release management. Shipping velocity improved 40% across cross-functional teams. Built on a Backstage-based engineering knowledge base with MCP connectors to JIRA, GitHub, Slack, and Teams; LLM workloads on OpenAI and Anthropic via A2A-style agent orchestration, implemented in Python/Bash tooling. Shipped with an enterprise AI governance layer: hallucination detection with knowledge-base remediation, model drift monitoring, and audit logging for regulated environments.
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Cover image for 90M registered users · 17
90M registered users · 17 countries · 99.99% uptime · $451M disbursed via DBT Led Everwell Hub from a monolith to a horizontally scalable microservices platform (Java/Python, Docker/Kubernetes, GCP, CI/CD, IaC). As chief architect I personally designed the service decomposition; as CTO I led ~50 engineers plus system-integrator partners and the WHO/Ministry of Health partnership. Deployed across 17 countries and scaled India's national TB platform (Ni-kshay) to ~90M registered patients and staff, with 6 lakh+ direct users, at 99.99% uptime, sub-second P95 latency, and 2h RTO / 4h RPO disaster recovery. The payments module disbursed $451M through direct benefit transfers. Released the platform as MIT open source with full wiki, runbooks, and API contracts — adopted by partner health systems since.
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Kafka(1)
Eric Dharmawan's avatar
Eric Dharmawan
West Jakarta, Indonesia
Building Modern Data Platforms: Snowflake, Kafka & dbt
New to Contra
Building Modern Data Platforms: Snowflake, Kafka & dbt
Cover image for I built this Event-Driven Streaming
I built this Event-Driven Streaming Pipeline to make live analytics feel effortless and instantaneous. Watch how the data flows from source to warehouse, and leave a comment if you think low-latency engineering deserves an award! 🏆✨ 🚀 The Stack: Apache Kafka | Confluent | Snowflake | Data Engineering We broke free from the chains of slow batch processing. By engineering a highly scalable stream using Kafka and Confluent, we ingested live event data directly into Snowflake—slashing latency and unlocking immediate downstream reporting for the business. 📊🔥
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Cover image for I built this Secure Enterprise
I built this Secure Enterprise Data Platform to make scalable analytics feel effortless and completely bulletproof. Watch how we structured the environments and leave a comment if you think clean data governance deserves an award! 🏆✨. The Stack: Snowflake | AWS S3 | SQL | RBAC We took raw cloud storage and transformed it into a governed analytics engine. By establishing strict role-based access control (RBAC) across dedicated dev and prod environments, we empowered multiple business teams with secure, maintainable, and production-ready data operations.
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Cover image for I built this Automated Python
I built this Automated Python ETL Pipeline to make data extraction and cleansing feel effortless and completely hands-free. Watch how we transformed messy inputs into cloud-ready tables, and leave a comment if you think wiping out manual data entry deserves an award! 🏆✨ 🐍 The Stack: Python | SQL | Cloud Analytics | APIs & Flat Files We rescued business teams from the nightmare of manual data preparation. By engineering a highly reusable, error-logged Python framework, we seamlessly ingested flat files, databases, and APIs. The result is a rock-solid batch processing engine that cleanses, validates, and delivers curated datasets straight to the cloud. ☁️📊
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Cover image for I built this High-Performance ClickHouse
I built this High-Performance ClickHouse Platform to make large-volume analytics feel effortless and instantly visual. Watch how we transformed raw records into Tableau-ready insights, and leave a comment if you think scalable architecture deserves an award! 🏆✨ 🚀 The Stack: ClickHouse | PostgreSQL | Amazon S3 | Python | Tableau We conquered massive volumes of attendance and payment data by building a unified ingestion workflow. By pulling multi-source data into strictly designed ClickHouse analytical tables, we delivered a reusable, highly scalable architecture that powers fast queries and interactive business dashboards without breaking a sweat. 💡⚙️
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Kafka(2)