Hire the Best Apache Hive Developers

Messy data slowing your team down? I build scalable ETL/ELT pipelines and modern cloud architectures on Azure, Databricks, Fabric, and Snowflake that turn raw, chaotic data into clean, analytics-ready systems fast and reliably. I bridge the gap between fragmented data sources and production-grade dashboards, seamlessly adapting to your existing infrastructure rather than forcing an expensive rebuild. What I Can Help You With: Data Warehouse & Lakehouse Architecture: Implementing Medallion design patterns (Bronze → Silver → Gold) using Delta Lake, Microsoft Fabric OneLake, and Snowflake. Scalable ETL/ELT Ingestion: Building automated, metadata-driven pipelines via Azure Data Factory, Fabric Pipelines, Databricks (PySpark/SQL), and dbt. Real-Time Data Streaming: Architecting low-latency workflows using Apache Kafka, Azure Event Hubs, and streaming engines. Database Design & Optimization: Performance tuning, indexing, and data modeling for PostgreSQL, Azure SQL, and cloud warehouses. Proven Project Highlights: Microsoft Fabric Incremental Pipeline: Built a control-table pattern using Get Metadata, Lookup, and ForEach loops to orchestrate zero-duplicate, quarterly ingestion from SharePoint into OneLake via Dataflow Gen2. Azure/Databricks Streaming: Developed a restaurant analytics platform processing 80,000+ events/day, cutting reporting lag from 6 hours to under 3 minutes. Kafka/Snowflake Pipeline: Engineered a real-time stock market data pipeline tracking 120+ tickers with under 8 seconds end-to-end latency. I write clean, documented code your team can maintain long-term and provide transparent daily updates. Message me with your data challenge and I’ll walk you through exactly how to solve it.

I am a tech-agnostic data engineer who enjoys solving complex data problems, finding patterns in messy datasets, and making sure the work actually supports the business. My focus is always on clean architecture, performance, and making data useful - not just moving it around.Here is what I have worked with: - Programming & Scripting: Python (Flask, FastAPI, Django, Selenium, PySpark), SQL - Data Engineering & Integration: Talend, Mage.ai, Apache Airflow, Airbyte, Databricks, Kafka, Debezium - Databases & Data Warehousing: Data Modeling, PostgreSQL, MySQL, Snowflake, Greenplum, MongoDB, Firebase, ClickHouse - Cloud & DevOps: AWS (Lambda, API Gateway, S3, RDS, SQS and more), Docker, Kubernetes, Azure DevOps, Terraform, CloudFormation - Security & Compliance: Secure and compliant pipeline design (HIPAA, GDPR, SOC 2), data governance, privacy-first architectures - Testing & Automation: Postman, Cypress.io, Swagger, Automated Data Workflows - Data Visualization: Power BI, Metabase, Google Data Studio, Grafana - Collaboration & Leadership: Cross-functional team mentoring, process optimization, data-driven decision-making

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.

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!

Data Engineer building the full-stack intelligence layer for modern businesses. I architect, implement, and maintain the complete data-to-insights value chain. I deliver three interconnected pillars: Reliable Data Infrastructure: Scalable pipelines, cloud data warehouses, and ELT processes. Actionable Business Intelligence: Dashboards, reports, and self-service analytics that inform strategy. Intelligent Automation: Production AI/ML models, RAG systems, and LLM integrations that predict outcomes and automate complex tasks. With deep expertise across the modern data stack (AWS/Azure/GCP, Snowflake, Databricks, Kafka, Airflow, dbt, Power BI/Tableau, Python ML stack, RAG/LLMs), I ensure your data assets are operational, insightful, and intelligent.