Hire the Best Apache Storm Developers

I build data platforms that work at scale and keep working as your business grows. Over the past 10 years I've served as the lead or founding data engineer across fintech, e-commerce, ride-hail, legal tech, and cybersecurity companies. That means I've designed systems from scratch, made architecture decisions with no one to fall back on, and delivered platforms that product teams actually use. Here's what I typically get hired to do: → Build greenfield data platforms on AWS or GCP from the ground up → Design and ship production ETL/ELT pipelines (Airflow, Dagster, dbt) → Set up scalable warehouses and governance (Snowflake, BigQuery, Redshift) → Implement real-time streaming pipelines (Kafka, Spark Streaming, CDC) → Build AI-powered data applications (RAG, LLMs, LangChain, vector DBs) → Fix broken or unreliable pipelines and make them production-grade → Architect cloud infrastructure on AWS, GCP, Azure (Terraform, Kubernetes) Recent work includes: - Led data platform engineering for a US e-commerce company processing billions of events daily. I re-architected ingestion pipelines, built Snowflake governance from scratch, introduced Prometheus monitoring and CI/CD standards across the platform. - Built a full data platform on GCP (BigQuery, Dataproc, Airflow) for a music streaming company. Firebase, AppsFlyer, and app store data all flowing into one warehouse within weeks. - Designed an AWS data platform for a ride-hail company managing 500+ streaming and 700+ batch jobs — including a self-serve portal that replaced multi-step CLI workflows for engineers. - Built a legal AI search engine using LangChain, Pinecone, and RAG — full pipeline from document ingestion to LLM-generated answers, deployed on AWS with auto-scaling. - Built an AI inventory insights agent for a US automotive company — multi-source data pipelines, real-time APIs, conversational interface. I work in English daily, communicate proactively, and deliver production- ready code — not prototypes. I'm used to working directly with CTOs and technical leads in US and European time zones. Tools I work with regularly: Python · SQL · Airflow · Dagster · dbt · Snowflake · BigQuery · Spark · Meltano · Kafka · AWS (S3, EMR, Glue, ECS, Lambda, EC2, EKS) · Databricks · GCP · Azure Terraform · Docker · Kubernetes · LangChain · FastAPI · MLflow · Weaviate, Celery If you're building a data platform, fixing one, or adding AI/ML capabilities to your stack, let's talk.

I help companies turn complex technical problems into reliable, production-ready systems — whether that's a backend built from scratch, an AI agent that automates a manual workflow, or infrastructure that scales without breaking. My background spans backend engineering, data pipelines, cloud architecture, and enterprise warehouse systems, so I can usually see the full picture of what a project needs — not just the code. 💻 Software Development - Production APIs and services in Java, Python, and Go using Spring Boot, Django, Flask, and FastAPI. Multiple projects go live with my help in architecture backend - Enterprise-grade platform development: at Fortna, I built and customized Warehouse Execution Systems (WES) and Warehouse Control Systems (WCS), handling order orchestration, inventory management, and real-time integration with conveyors, sortation, and robotics in automated fulfillment centers - WES/WCS technical support — troubleshooting production issues, tuning order flow logic, resolving integration faults between software and material-handling equipment, and providing ongoing operational support for live warehouse systems 🤖 AI Agents & Automation - Designing agent workflows that handle real business tasks: data extraction, document processing, customer support, internal tooling, reporting - Building with Claude, Cursor, Codex, and LangChain-style orchestration to ship faster and cut manual work - Wiring AI agents into your existing stack — databases, APIs, dashboards — so automation fits your workflow instead of sitting on top of it 🗄 Databases & Data Engineering - Schema design and optimization across MySQL, MongoDB, Neo4j, and Redis - ETL pipelines and data warehouses, on-prem or cloud - Web scraping and data processing with Selenium, BeautifulSoup, Scrapy, Playwright, orchestrated via Airflow or Dagster ☁️ Cloud & DevOps - AWS and GCP deployment and management - Infrastructure as code with Terraform - Kubernetes for scalability - Monitoring with Prometheus and Grafana 🔐 Security & Digital Signatures - Digital signature workflows: PDF signing, PKI, CSC API - Tooling experience with iText, PDFBox, pyHanko, DSS Framework - Cryptography and HSM integration for compliance-driven projects

I'm a Senior Data Engineer with 8+ years of strong technical expertise in building reliable and scalable data infrastructure, from data ingestion to transformation to warehousing, streaming, and data analytics, specializing in dbt, Snowflake, Airflow, Databricks (and more) across AWS, Azure, and GCP, with robust ELT and ETL pipelines. If your data pipelines are brittle, your data warehouse is slow, or your data was never built to scale, that is exactly what I fix, with fault tolerance, observability, and audit-ready quality engineered in from day one. I cover the full data engineering lifecycle: batch and real-time data pipelines, Modern Data Stack builds, lakehouse architecture, cloud and warehouse data migration, governance, and the data foundations that feed modern systems. 🎯 Core Expertise: ✅ Data Pipelines & Orchestration: End-to-end batch and real-time pipelines with Apache Airflow, Dagster, Prefect, AWS Step Functions, and Azure Data Factory. Idempotent, schema-drift tolerant, and monitored so failures surface before they reach your stakeholders. ✅ Cloud Warehousing & Lakehouse: Snowflake, BigQuery, Amazon Redshift, Databricks, and Microsoft Fabric, with Delta Lake and Apache Iceberg lakehouse foundations governed through the Glue Data Catalog and Lake Formation, with Athena and Redshift Spectrum for serverless queries, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark on EMR and AWS Glue, Star Schema and dimensional modeling, analytics engineering best practices, full test coverage, and CI/CD for data models. ✅ Streaming & Real-Time Analytics: Distributed streaming with Apache Kafka, Flink, Spark Structured Streaming, Kinesis, and Pub/Sub, including exactly-once semantics, dead-letter queues, CDC, and end-to-end latency guarantees. ✅ Data Ingestion & Integration: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, and custom CDC pipelines for near-real-time sync across structured, semi-structured, and unstructured sources. ✅ Data Quality, Governance & Observability: Automated data quality frameworks, SLA monitoring, auditable lineage, data catalog and metadata management, and observability that catches bad data early. ✅ Cloud Migration & Modernization: Zero-downtime migration handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime, replacing brittle ETL and ELT with a clean Modern Data Stack. ✅ AI-Ready Data Infrastructure: Pipelines engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ------------------------------------------------------ ⚙️Tech Stack: ⚡ Warehouses & Lakehouse: Snowflake | BigQuery | Redshift | Databricks | Microsoft Fabric | Athena | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | AWS Glue | EMR | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory | Step Functions ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Governance & Catalog: Glue Data Catalog | Lake Formation | Unity Catalog | Microsoft Purview | Dataplex ⚡ Cloud: AWS | GCP | Azure ⚡ Languages: Python | SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) | FastAPI ⚡ Databases: PostgreSQL | MySQL | SQL Server | DynamoDB | MongoDB ⚡ BI & Reporting: Looker | Tableau | Power BI | GA4 | Metabase | Superset | Streamlit | Grafana ------------------------------------------------------ ⭐ What Clients Say: 🏅 "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS 🏅 "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm 🏅 "Clean architecture, solid dbt models, and Airflow pipelines running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup 🏅 "We came to him with a Spark pipeline costing us a fortune and delivering stale data. He restructured the workflow logic and cut processing time by 70%." – Leo, Head of Analytics, HealthTech SaaS ------------------------------------------------------ 🏆 TOP RATED PLUS | EXPERT-VETTED | Top 1% on Upwork | 8+ Years Experience | 100% Job Success 🚀 Ready to build a scalable, production-ready data infrastructure to turn your raw data into reliable, actionable business insights? Click the 'Invite to Job' button on the top right, and let's discuss your data pipeline!

I'm Huanqing Zhu, and you can call me Fusion. With over 10 years of hands-on Java development experience—including 6 years dedicated to big data processing and visualization—I’ve built my expertise by staying rooted in frontline coding, even as my responsibilities have grown. A key pillar of my technical toolkit is ‌6 years of production-grade Rust development experience‌, complemented by proficiency in Java, Scala, JavaScript, HTML5, and a full stack of big data and cloud-native technologies: Apache Spark, Hadoop, Hive, Flume, HBase, Storm, Kafka, DataX, ECharts, Docker, Kubernetes, and Linux. What sets me apart is that I’ve never stepped away from writing production code, even as I’ve taken on leadership and architectural roles: As a ‌hands-on Big Data Developer‌, I’ve built robust data ingestion utilities (including the open-source DataXServer on GitHub) and real-time page click analytics systems, directly coding pipelines to pull data from RDBMS, NoSQL databases, and file storage into production environments. As a ‌Big Data Architect‌, I’ve led platform design while still contributing core code, using Hadoop, Spark, Flink, and ElasticSearch to build scalable data infrastructure—no abstract planning here; I’ve written the critical components that power these systems. As a ‌Rust Specialist‌, my 6 years of experience spans building high-performance, low-latency systems. I’ve used Rust to optimize data processing pipelines, cut latency by up to 40% in high-throughput scenarios, and deliver systems that run 24/7 with zero critical errors. As a ‌Team Leader‌, I’ve managed full-stack teams (Java, front-end, QA, operations) while still pairing with developers on complex code reviews and contributing to high-priority features, ensuring I stay connected to the day-to-day challenges of software delivery. I also bring deep experience in microservices architecture and cloud-native containerization, and my cross-language expertise lets me bridge gaps between Java-based enterprise systems and Rust-powered high-performance components. If you’re looking for a professional who combines strategic vision with the grit to deliver production-ready code—someone who can architect a system, and write the Rust or Java code that makes it run—I’m the candidate for you. Thank you for reviewing my profile. I’m eager to discuss how my hands-on experience can add value to your team.

I'm a Full-Stack Data with Python Java Developer with over 8+ years of industry experience. I excel in all aspects of Java application development, from design and development to deployment. I have a proven track record of building robust and Scalable applications across diverse domains. My Expertise Full-Stack Development: Java: Core Java, multithreading, OOP, Spring, Hibernate, Struts, J2EE. Frameworks & Libraries: Spring, Hibernate, JavaFX, Apache Struts. Microservices Architecture: Spring Cloud, Netflix OSS, service discovery, load balancing, tolerance mechanisms. Web Development: JSP, Servlets, RESTful APIs, Angular, React, HTML, CSS, JavaScript. Databases: MySQL, PostgreSQL, MongoDB, Redis. Testing & Debugging: JUnit, Mockito. I also use GitHub Copilot and Claude Code to deliver the best solutions. ✔ LLM integrations (OpenAI, Claude, etc.) ✔ AI-assisted workflow automation ✔ Prompt engineering & optimization ✔ RAG based enterprise search solutions ✔ AI code acceleration using tools like Cursor ✔ Designing AI-ready Microservices architecture Java Solution Architect: Designing scalable solutions with Java, Spring, Hibernate, and microservices. DevOps: Cloud setup, infrastructure management, CI/CD. Proficient in AWS (Lambda, EC2, S3, Redshift, SageMaker, Cognito) and GCP (VMs, Google cloud storage). Domain Experience: Insurance, finance, healthcare, e-learning, e-commerce, travel, real estate, logistics, supply chain, social networking, and education. CRM and ERP systems.

Are your logs getting expensive, Elasticsearch slowing down, or monitoring systems hard to manage? I help businesses build and optimize scalable, cost-efficient logging and monitoring systems using ELK Stack and modern DevOps tools. With 5+ years of experience in production environments, I focus not only on performance and reliability — but also on reducing infrastructure and storage costs without sacrificing visibility. What I can help you with: ✔ Elasticsearch cluster setup, scaling & performance tuning ✔ Cost optimization (index lifecycle, shard strategy, storage reduction) ✔ Query optimization to reduce load and improve response time ✔ Logstash pipelines (efficient parsing, reduced ingestion overhead) ✔ Kibana dashboards, RBAC, and alerting ✔ Grafana monitoring & visualization ✔ Kubernetes & Docker deployments (optimized resource usage) ✔ NGINX configuration and performance tuning ✔ AWS infrastructure setup with cost-aware architecture ✔ Automation using Ansible and CI/CD Why work with me? ✓ I reduce Elasticsearch storage and compute costs (ILM, rollover, retention tuning) ✓ I build systems that are fast, scalable, and efficient ✓ I understand real-world production and SOC environments ✓ Clean, maintainable architecture with proper documentation If you’re looking to cut costs, improve performance, or fix an existing setup, I can help you get it done the right way.