Hire the Best MapReduce Specialists

Most data pipelines don’t fail because of code. They fail because they weren't built for scale. With 5+ years of experience engineering data systems at companies like Danone and Zurich, I help businesses transform fragile prototypes into resilient, production-grade infrastructure. I don’t just move data; I build the "Source of Truth" that leadership and AI systems actually trust. ➔ Productionizing AI Pipelines: Hardening Python prototypes into scalable RAG and LLM infrastructures (Azure). ➔ Infrastructure-as-Code: Building automated, modular ETL/ELT pipelines that don't require daily manual fixes. ➔ The "One-Source" Dashboard: Integrating messy data from APIs, SaaS (Shopify, HubSpot), and databases into clean Snowflake/BigQuery layers. ➔ Performance Recovery: Optimizing slow SQL queries and high-cost cloud warehouses to save you thousands in monthly spend. ➔ Technical Writing for Data & AI Teams: Creating product documentation, implementation guides, architecture documentation, data dictionaries, knowledge bases, and thought leadership content that makes complex systems easier to understand and adopt. 🛠 Tech Stack Languages: Python (FastAPI, Pandas, PySpark), SQL Data Engineering: ETL/ELT Pipelines, Data Warehousing, Data Modeling, Data Quality, Data Governance Cloud & Warehousing: Snowflake, BigQuery, Databricks, Azure Data Factory, Azure Data Lake, AWS (S3, Athena, Glue) Orchestration & Transformation: Apache Airflow, dbt Analytics & BI: Tableau, Power BI Development & Collaboration: Git, GitHub, VS Code Data Ops: API Integrations, Data Validation, Workflow Automation Technical Writing: Product Documentation, API Documentation, User Guides, Knowledge Bases, Data Dictionaries, Technical Blog Content ✅ Why Me? 5+ Years Experience: I've seen what breaks at the enterprise level and how to prevent it in your startup. Hands-On Builder & Technical Writer: I can both build the system and explain it clearly to engineers, stakeholders, and customers. Speed over Perfection: I focus on shipping high-impact systems that drive revenue, not just technical documentation. Transparent Communication: You get regular updates and a partner who challenges requirements to find better solutions. Ready to clean up your data debt?

I'm a dynamic data expert with a proven ability to deliver short- and long-term projects in the realms of data engineering, data warehousing, and business intelligence. My passion is to partner with my clients to deliver top-notch, scalable data solutions to provide immediate and lasting value. I specialise in the following data solutions: ✔️ Data strategy advisory & technology selection/recommendation ✔️ Building data warehouses using modern cloud platforms and technologies ✔️ Creating and automating data pipelines, real-time streaming & ETL processes ✔️ Data Cleaning and Processing. ✔️ Data Migration (Heterogeneous and Homogeneous) Some of the technologies I most frequently work with are: ☁️ Cloud: GCP, AWS & Azure 👨‍💻 Databases: BigQuery, Google Cloud SQL, SQL Server, Snowflake, PostgreSQL, MySQL, S3, Google Cloud Storage, Azure Data Lake Storage. ⚙️ Data Integration/ETL: Matillion ETL for Snowflake/BigQuery, Apache Airflow( Google Cloud Composer, AWS MWAA, Astronomer), Azure Data Factory, Azure Logic Apps, Dagster and DBT. 🔑 Scripting - Python for API Integrations and Data Processing. 🤖 Serverless Solutions - Google Cloud Functions, Lambda Functions and Azure Functions. 📊 Dashboard Reporting - Microsoft Power BI, Apache Superset, Metabase, Looker Studio and Plotly. 🛠 Others - Process Automation in Python, N8N and Much More. =What my clients say about me== ------------------------------------------------------ "Waqar is very clued up, and he thinks outside the box. He is always looking for ways to implement the solution efficiently and cost-effectively. His communication skills are excellent. He is willing to go the extra mile. He has been a pleasure to work with. I will be working with him in the future." ⭐⭐⭐⭐⭐ ------------------------------------------------------ "Waqar has expert-level knowledge of Google Cloud and knows how to make cloud technologies work effectively for the marketing domain." ⭐⭐⭐⭐⭐ ------------------------------------------------------ I am highly attentive to detail, organised, efficient, and responsive. Let's get to work! 💪

I am a senior Data Engineer with 6+ years of experience designing and building large-scale data pipelines and analytics platforms across finance, consulting, and enterprise domains. Currently working as a Sales Data Analytics Engineer at Russell Investments, I design and maintain high-performance data pipelines processing millions of records daily using Python, Pandas, Polars, Spark, and Snowflake. I have strong experience in building end-to-end ETL/ELT systems, optimizing SQL for sub-second query performance, and automating workflows using Apache Airflow and CI/CD. Previously at Cognizant, I built real-time and batch data platforms using Kafka, AWS Glue, S3, Snowflake, and Vertica, handling 10M+ records daily and achieving 99.9% pipeline reliability. I specialize in data modeling, performance tuning, cost optimization, and building production-grade analytics systems. Core expertise: • Data Warehousing: Snowflake, Vertica, SQL Server • ETL & Orchestration: Airflow, AWS Glue, Control-M • Big Data: Spark, Kafka, PySpark • Programming: Python, SQL (Advanced), Pandas, Polars, NumPy • Cloud: AWS (S3, Redshift, Glue), CI/CD, Git • Data Modeling & Optimization What clients get when working with me: ✔ Scalable, reliable data pipelines ✔ Optimized Snowflake & SQL performance ✔ Clean, production-ready Python code ✔ Automated workflows with monitoring ✔ Clear communication and on-time delivery I help startups and enterprises with: End-to-end data pipeline development Snowflake & cloud data warehouse design SQL performance tuning Real-time & batch ETL systems Data migration & modernization Analytics-ready data models

Hi, I'm Owais. I've spent 7+ years building data systems across enterprise and startup environments. I've set up entire data platforms from end-to-end on AWS, Azure, and GCP, including in SOC 2 and HIPAA environments. I've built ETL pipelines that don't break at 3 AM, modeled warehouses that survive contact with real business logic, and shipped dashboards people actually open. These days I help growth-stage startups build analytics platforms around AI without bleeding money on it. Natural language to SQL, agents that surface insights instead of waiting to be asked, automated anomaly detection, the kind of features your customers are starting to expect. Most AI-in-analytics projects don't fail because the model isn't good enough. They fail because the foundation underneath it is a mess. A well-documented dbt project does more for AI accuracy than a bigger LLM, and if your dashboards aren't getting opened, adding AI on top doesn't change anything. If you're trying to build AI-powered analytics and want someone who's done the unglamorous work that makes it actually useful, let's talk.

Most data pipelines don't fail loudly. They rot quietly. A job starts silently skipping rows. A dashboard still loads, but the numbers stopped matching reality weeks ago. The warehouse bill creeps up because nobody ever tuned the queries. Eventually someone exports to a spreadsheet "just to be sure" and that's the moment the system stopped being trusted. That's the work I do: build data pipelines and warehouses people actually rely on, and fix the ones that quietly stopped working. Over the last 3 years I've delivered 55+ data projects across finance, healthcare, energy, and e-commerce, from one-off ETL jobs to platforms processing billions of records a day. I work GCP-first (BigQuery, Airflow, dbt, Dataflow), and I'm comfortable across AWS, Postgres, and the messy real-world stack most teams actually have. What I build: - End-to-end ETL/ELT pipelines in Python, SQL, Airflow, and dbt - BigQuery / Snowflake / Redshift warehouses and data models that stay clean as they grow - Migrations off legacy jobs and on-prem databases — without losing data in the move - Metabase, Looker Studio, and Power BI dashboards your team will actually open - Query and cost optimization when your warehouse bill stops making sense - API integrations with proper logging, retries, and checkpoints, so failures are visible instead of silent You probably need me if: - Your pipelines break and you hear it from a stakeholder, not an alert - Reports run slow, cost too much, or quietly disagree with each other - A previous developer left and nobody fully understands the setup anymore - You're scaling fast and the current data stack is starting to crack A few real results: - Architected pipelines processing 5B+ records daily at 99% reliability - Cut a client's warehouse costs ~50% by migrating legacy jobs to BigQuery - 4× throughput and 70% faster ingestion on an API pipeline pulling 2K+ domains a day - 40% faster pipeline runs through Airflow optimization How I work: a clear yes/no on feasibility before you commit, regular updates, and no disappearing mid-project. Most clients come back — usually because fixing one thing surfaces the next. If that sounds like your situation, send a short note on what's breaking or what you're trying to build, and I'll tell you straight what it'll take.

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.