Hire the Best Apache Spark MLlib Specialists

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.

✅ Top Rated Plus | 100% JSS | 4x Certified (AWS SA Pro, GCP Pro Architect, Snowflake) | BITS Pilani MTech Data Science | Full Stack Developer & Data Engineer | React, Python, Node.js, Spark | $20K+ earned | 1,845+ hours I build full-stack web applications and data engineering systems that go to production, not to demo day. SaaS MVPs, Spark-based ETL pipelines, cloud architecture on AWS and GCP, I handle both the application layer and the data infrastructure behind it. 🔹 Full-Stack SaaS & Web Application Development React, Next.js, Node.js, and Python backends for SaaS platforms, dashboards, internal tools, and customer-facing apps. MVP to production on AWS/GCP with CI/CD, automated testing, and monitoring from day one. 19 Upwork contracts delivered with structured milestones. 🔹 Data Engineering & ETL Pipeline Architecture End-to-end data pipeline design with Apache Spark, PySpark, Scala, Snowflake, and Airflow. Batch and streaming ETL processing millions of records per run. Data lake architecture, warehouse modeling, analytics-ready output layers. 8+ years building production Spark + Cassandra systems at enterprise scale. 🔹 Cloud Architecture & Infrastructure (AWS + GCP) 4 cloud architecture projects on Upwork, all rated 5.0. $2,300 CloudStack design. AWS architecture advisory. EC2, Lambda, S3, RDS, EMR, Redshift on AWS. BigQuery, Dataflow, Cloud Functions on GCP. Terraform for IaC, Docker and Kubernetes for orchestration, zero-downtime deployments. 🔹 API Development & Backend Systems REST API and GraphQL backends with Node.js, NestJS, FastAPI, and Django. Microservices, Redis caching, WebSocket integrations, Stripe payment APIs, OAuth/JWT authentication. Backend services handling concurrent users at production scale. 🔹 Database Design & Data Modeling PostgreSQL, MongoDB, MySQL, Cassandra, DynamoDB, Redis. Schema design, query tuning, indexing, partitioning. Star and snowflake schemas, slowly changing dimensions, SQL optimization for analytics. Architecture decisions balancing performance, throughput, and cost. 🔹 AI Integration & Intelligent Applications OpenAI API, Hugging Face, NLP pipelines, chatbot systems, text extraction and summarization. Delivered NLP processing on Upwork. AI-powered features built into SaaS products as production features, not standalone experiments. 🔹 Real-Time Processing & Event-Driven Systems Kafka for event-driven architectures, change data capture, WebSocket dashboards, streaming pipelines for near-real-time analytics. Application events connected to data warehouse layers. 🔹 Frontend Performance & TypeScript Engineering React and Next.js with SSR/SSG for SEO-friendly rendering. TypeScript full stack. Core Web Vitals optimization, Tailwind CSS, responsive design. Fast-loading frontends that rank and convert. 🔹 DevOps, CI/CD & Production Systems Docker, Kubernetes, Terraform, GitHub Actions, GitLab CI. Serverless with AWS Lambda and GCP Cloud Functions. Monitoring, logging, alerting for production. Zero-downtime deployment strategies. 🔹 Technical Consulting & Architecture Advisory TypeScript and AWS Lambda tutor on Upwork, rated 5.0 over 13 hours. Cloud migration advisory, system design review, code audits, performance optimization, engineering mentorship. 📊 AWS Solutions Architect Professional + Associate (Dec 2026) | GCP Pro Cloud Architect (Jul 2026) | Snowflake Core (Jan 2026) 📊 MTech Data Science, BITS Pilani, ranked top 5 engineering institutions in India 📊 19 contracts, 100% JSS, Top Rated Plus, 1,845+ hours tracked, $20K+ earned 📊 "Jay's expertise brought the architecture design to life in ways I hadn't imagined" (5.0 rated) 📊 8+ years: React, Node.js, Python, Java, Scala across SaaS, healthcare, fintech, enterprise → Day 1: Requirements call + architecture proposal with tech stack rationale → Week 1: Sprint development, daily Loom/Slack updates, working code shipped → Ongoing: Weekly demos, priority reviews, transparent tracking, full documentation → Delivery: Documented code, CI/CD configured, deployment guide, 2-week post-launch support Full Stack: React, Next.js, Node.js, NestJS, Express, TypeScript, JavaScript, Python, FastAPI, Django Data: Apache Spark, PySpark, Scala, Snowflake, Airflow, Kafka, ETL, dbt, SQL, BigQuery Cloud: AWS (Lambda, EC2, S3, RDS, EMR, Redshift), GCP (BigQuery, Dataflow), Docker, Kubernetes, Terraform DB: PostgreSQL, MongoDB, MySQL, Redis, Cassandra, DynamoDB, Supabase AI: OpenAI API, Hugging Face, NLP, LLM Integration, TensorFlow, PyTorch 💬 Message me with your project scope or data challenge. I respond within 4 hours with a free assessment and can start within 48 hours.

Hands on Data architect & Lead data engineer, with 12+ years of experience in designing & building end to end high velocity, high volume peta byte real time & batch data platforms from scratch on clouds & on-prem. Kubernetes native development from the beginnig. I develop distributed & scalable back-end systems using using languages like goLang, Rust & python. Lately Started working on integration of AI, RAG, MCP Servers & MLops Platforms into data platforms. Developed self hosted llms applications using Ollama and llm observability using langfuse. Mordenize existing data platforms with AI first approach. Hybrid semantic mapping layers or unstructured and structured data using heuristics, memory and LLMs. Built & worked on peta byte scale streaming, batch data & AI platforms in top companies. An open source contributor to data technologies & products like Airbyte etc. Love working on database internals, performance and optimizations. I have experience working with telemetry data, payments data, video data, sports data, eCommerce data & affiliate marketing data, logs data, clickstream data. Skill Set: Big Data Technologies: Spark, Kafka, Flink, Presto, Dremio, Hudi, Deltalake Data warehouses: Snowflake, Druid, Clickhouse, Redshift, SingleStore(Memsql), Quest Databases: Postgres, Mysql, Cassandra, DynamoDB, DuckDB Programming languages: Golang, Python, Rust, Scala, Java Visualization: Tableau, Apache Superset, Zoomdata Data Technologies - Airbyte, Fivetran, Dagster, Airflow, Nifi, Kubeflow, ElasticSearch, OpenSearch Platforms: Databricks, Snowflake, Cloudera, Supabase, Aiven Ops: Kubernetes, Docker Cloud: AWS, GCP, Azure

Senior Data Engineer and AI Engineer with 15+ years in data engineering, AI engineering, and cloud data platforms. I build production data pipelines, ETL workflows, LLM and RAG systems, and machine learning infrastructure on AWS, GCP, Azure, Snowflake, Databricks, and Apache Spark. Most of my data engineering work is end to end. I take a messy data problem, or an AI feature that "almost works," and turn it into something reliable that runs in production without someone babysitting it. After 15+ years as a data engineer, you learn the hard part is rarely the model or the framework. It's the data, the edge cases, the pipelines, and keeping the system maintainable once you've handed it over. As an AI engineer I treat LLM and RAG work the same way: take a prototype that almost holds together and make it a production AI system that survives real traffic, real users, and real data. Recent data engineering and AI engineering projects: - An AI inbound phone system built on Twilio with OpenAI Whisper and GPT-4, handling real-time voice intake and call routing. - Enterprise Power BI data models for healthcare and financial reporting, around 30 tables and 40+ DAX measures, including IFRS 9 staging, RAROC, and NIM trends. - HL7 FHIR R4 integrations with Epic, Cerner, and Athenahealth for a clinical AI platform. - Cut LLM inference cost on a high-volume voice product by 25% by reworking how it used the OpenAI Realtime API and its per-turn token replay. Data engineering: Apache Spark, PySpark, dbt, Apache Airflow, ETL and ELT pipelines, data warehousing, data modeling, Snowflake, BigQuery, Redshift, Databricks, Delta Lake, Kafka, Kinesis, Fivetran. AI engineering and machine learning: OpenAI GPT-4o, Claude, Gemini, LangChain, LlamaIndex, RAG pipelines, AI agents, prompt engineering, vector search (Pinecone, Weaviate, pgvector), PyTorch, TensorFlow, scikit-learn, MLflow, model deployment. Cloud and DevOps: AWS (Glue, EMR, Lambda, Redshift, SageMaker, Athena), GCP (BigQuery, Dataflow, Vertex AI), Azure (Synapse, Data Factory, Azure ML), Terraform, Docker, Kubernetes, GitHub Actions. Automation and integration: n8n, Make, Power Automate, REST and GraphQL APIs. Governance and compliance: GDPR, HIPAA, SOC 2, RBAC, PII masking, encryption, data lineage. Languages: Python, SQL, Scala, PySpark, FastAPI, Flask. How I work: I'd rather ask the right questions up front than build the wrong thing quickly. I'll tell you when something is a bad idea, give you timelines I can keep, and leave you with code and documentation your own team can maintain. I've delivered data engineering and AI engineering projects for startups and enterprises across the US, Europe, and Asia. If you're looking to hire a data engineer or AI engineer who can own the work end to end, from raw data pipeline to production AI system, I'm available now. Tell me what you're building and I'll give you a straight answer on how I'd approach it.

Your data is scattered across APIs, databases, and third-party tools and right now it takes your team hours to pull reports that should take seconds. I fix that. I'm Daniyal, a Data Engineer who builds production-grade ETL/ELT pipelines that collect, transform, and deliver clean data to your dashboards automatically. My pipelines run 24/7 and scale with your business. Recent Results: • BigQuery warehouse ingesting 50,000+ daily records client margins up 22% • Airflow ETL processing 600,000+ weekly records for a real estate platform • Automated data pipeline generating 600+ qualified leads in 45 days ($75K in new revenue) • Cut manual reporting from 8 hours/week to zero with scheduled orchestration What I Build: • ETL/ELT pipelines on BigQuery, Snowflake, and Redshift • Apache Airflow DAGs for scheduled, monitored data orchestration • Data warehouse architecture with dbt transformations • Real-time and batch data ingestion from APIs, databases, and flat files • Monitoring, alerting, and data quality checks built into every pipeline Tech Stack: Warehouses: BigQuery, Snowflake, Redshift Orchestration: Apache Airflow, dbt, Prefect Processing: PySpark, Pandas, SQL, Kafka Cloud: AWS (S3, Glue, Lambda, Redshift), GCP (BigQuery, Dataflow, Composer) Infrastructure: Docker, Terraform, CI/CD Top Rated • 100% Job Success Score • Response within 2 hours Full documentation, clean handoff, and 30-day post-delivery support on every project. Send me your data challenge and current stack. I'll reply within 2 hours with a clear plan.

🏆 TOP RATED PLUS || Top 1% on Upwork || Expert Vetted || 8+ Years of Experience || 100% Job Success Most data teams are held back by unreliable pipelines, warehouses they cannot trust, and data infrastructure that was never built to scale. That's exactly what I fix. As a Senior Data Engineer, I don't just write SQL and call it a pipeline. I architect end-to-end data systems where reliable ingestion feeds into clean, versioned transformations that power decisions your business can act on. My approach prioritizes fault tolerance, scalability, and observability across both batch processing and real-time analytics workloads. This ensures your data infrastructure is not just functional, but resilient and audit-ready. Whether you need cloud data migration, data platform modernization to a Modern Data Stack (Snowflake/dbt/Airflow, Microsoft Fabric), or streaming analytics infrastructure, I deliver production-grade systems that help technical founders and data teams eliminate pipeline debt, automate complex data workflows, and build scalable infrastructure ready for AI workloads. -------------------------- Where I make the biggest impact: ✅ I lead data migration and data platform modernization projects, replacing brittle ETL and ELT pipelines with a Modern Data Stack built on Snowflake, dbt, Airflow, and Microsoft Fabric. ✅ Every engagement includes Medallion Architecture design, full test coverage, CI/CD for data models, data lineage tracking, and documentation that outlasts the project. ✅ I design data pipelines for both batch processing and real-time analytics, idempotent, schema-drift tolerant, and monitored through data observability frameworks, so failures are caught before they reach your stakeholders. ✅ Warehouse models are built to serve the business: Star Schema, dimensional modeling, dbt projects, analytics engineering best practices, and a metrics layer backed by a data catalog and metadata management. ✅ I architect distributed systems for big data and streaming analytics, including Kafka, Flink, Spark Structured Streaming, exactly-once semantics, dead-letter queues, and end-to-end latency guarantees. ✅ AI data pipelines are engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ✅ I bring governance to data platforms through data mesh, data catalog implementation, metadata management, and data integration across systems. ✅ Data quality and data reliability are enforced end to end, with automated frameworks, SLA monitoring, auditable lineage, and observability that catches bad data before it reaches your stakeholders. ✅ I build AI-ready data infrastructure and lakehouse foundations, Delta Lake, Apache Iceberg, cloud data architecture, and CDC pipelines for near-real-time sync. ✅ Cloud data migration is handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime. -------------------------- What I Build With: 🗄️ Warehouses, Lakehouses & Data Lakes: Snowflake, BigQuery, Redshift, Databricks, Microsoft Fabric, Delta Lake, Iceberg ⚙️ Transformation: dbt (Core & Cloud), SQLMesh, Spark, PySpark, Star Schema, Medallion Architecture 🔁 Orchestration: Airflow, Dagster, Prefect, Azure Data Factory, Microsoft Fabric 📨 Streaming: Kafka, Kinesis, Pub/Sub, Flink, Fabric Eventstream 🔗 Ingestion: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, CDC pipelines ☁️ Cloud: AWS, GCP, Azure 🐍 Languages: Python, SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) 🗃️ Databases: PostgreSQL, MySQL, SQL Server, DynamoDB, MongoDB 📊 BI & Reporting: Looker, Tableau, Power BI, Metabase, Superset -------------------------- 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 ⭐ "Adarsh built our entire data platform from the ground up. Clean architecture, solid dbt models, and Airflow pipelines that have been running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Marcus, Director of Data Engineering, E-commerce Startup ⭐ "We came to Adarsh with a Spark pipeline that was costing us a fortune and delivering stale data. He diagnosed the bottlenecks, restructured the job logic, and cut our processing time by 70%. Technically sharp, communicates clearly, and delivers without hand-holding." – Leo, Head of Analytics, HealthTech SaaS -------------------------- 🚀 Let's Build Your Data Foundation 📩 If your data infrastructure needs to be faster, cleaner, and something your team can trust, send a quick message about your project and I'll take it from there.