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🚀 Data Engineer & Solution Architect | Scaling Data Platforms 10× Without Breaking Them I design data systems that don’t just run, they scale, perform, and stay reliable under real-world pressure. With 7+ years building enterprise-grade platforms, I’ve seen the same story repeat: A pipeline works at 10M records… then collapses at 100M. Costs spiral. Latency explodes. Nobody wants to touch the legacy system. That’s where I come in. 🧠 What I Actually Deliver I architect cloud-native data platforms built for tomorrow not quick fixes for today. ✔ Migrate fragile legacy systems to modern, resilient architectures ✔ Design scalable data lakes and lakehouses ✔ Optimize pipelines bleeding money and compute ✔ Build real-time analytics for mission-critical decisions ✔ Create foundations ready for AI/ML workloads Result: Systems that grow with your business instead of holding it back. ⚙️ Deep Technical Expertise Across the Stack ☁️ Cloud Platforms AWS: Glue, EMR, Redshift, Kinesis, S3, Lambda, Lake Formation, DMS, MSK, RDS Azure: Data Factory, Synapse, Databricks, DevOps GCP: Dataflow, Cloud Functions, Cloud Storage 🔥 Big Data & Streaming Apache Spark (Scala & PySpark) • Kafka • Kinesis • NiFi • Hadoop Ecosystem • Airflow • Delta Lake 💻 Programming Python • Scala • SQL • Shell • Java 🗄️ Databases & Storage PostgreSQL • MySQL • Oracle • SQL Server • MongoDB • Cassandra • DynamoDB • Elasticsearch 🛠️ DevOps & Infrastructure Docker • Kubernetes • OpenShift • Terraform • Jenkins • Ansible • Git 📊 Observability & Governance CloudWatch • ELK • Grafana • Athena IAM • Lake Formation • Encryption • Audit Logging • Okta • Cognito 🏢 Enterprise Experience That Matters I’ve delivered production systems for Fortune 500 organizations across finance, energy, hospitality, and SaaS handling hundreds of millions of records daily. From ingestion → transformation → real-time analytics → security → DevOps automation — I design the full lifecycle. 🏆 Proven Impact ✔ Re-architected legacy pipelines → 5× performance boost & 60% cost reduction ✔ Built event-driven systems processing 500M+ records/day ✔ Delivered secure data lakes with row-level governance ✔ Reduced MTTR by 70% with end-to-end observability ✔ Led zero-downtime cloud migrations ✔ Secured $2B+ transaction data with encryption platforms 🤝 Best Fit For Organizations That Need 🔹 Cloud migration with strong architectural guidance 🔹 Performance or scalability bottlenecks 🔹 Data platforms for AI/ML initiatives 🔹 Multi-cloud or hybrid strategies 🔹 Long-term reliability over quick hacks ⚠️ Not a Fit For ❌ One-off scripts or basic SQL tasks ❌ Temporary data cleanup work ❌ Short-term patch solutions I focus where architecture decisions create lasting business value. 💬 What Clients Value Most Clear thinking on complex problems Communication executives understand Engineering teams trust Systems built to last 👉 If your data platform needs to scale, stabilize, or modernize then let’s talk.

I’m a Data Engineering Professional with 4+ years of experience delivering innovative, scalable, and efficient solutions for complex data challenges. My expertise spans Big Data, Data Warehousing, Cloud Computing, and Data Analytics, ensuring seamless ETL pipelines and actionable insights for clients worldwide. 🌟 My Mission: To help businesses leverage data for smarter decisions, optimized workflows, and measurable results. 🛠️ Core Competencies 📊 Big Data & Data Engineering Proficient in Apache Spark, Hadoop, MapReduce, Hive, Kafka, Airflow, and Snowflake. Real-time and batch data processing expertise using Spark Streaming and Flink. Skilled in tools like Presto, Cloudera Manager, StreamSets, and Zookeeper. ☁️ Cloud Platforms AWS: EC2, S3, RDS, EMR, Redshift, Lambda, Glue, Kinesis, Athena. Azure: Azure Databricks, Synapse Analytics, Data Factory, Data Lake. GCP: BigQuery, Dataflow, and other scalable services. 📈 Data Visualization & Business Intelligence Expertise in crafting dashboards with Tableau, Power BI, and Grafana for actionable insights. 💾 Databases Mastery of SQL and NoSQL databases, including SQL Server, PostgreSQL, MongoDB, Cassandra, and HBase. 💻 Programming & DevOps Advanced coding skills in Python, Scala, and Java for ETL workflows and automation. Experience with Docker, Kubernetes, and other DevOps tools for smooth deployment. 🌟 Why Choose Me? ✅ Proven Track Record Successfully designed and implemented end-to-end pipelines, processing massive datasets with exceptional performance. ✅ Cloud Expertise Extensive hands-on experience with AWS, Azure, and GCP, ensuring cost-effective and scalable solutions. ✅ Business-Driven Solutions Focused on aligning technical implementations with your business goals to maximize value. ✅ On-Time Delivery Reliability and adherence to deadlines without compromising quality. 💼 Let’s Collaborate! Looking for a dedicated, detail-oriented, and highly skilled Data Engineer to transform your data strategies? Let’s connect and build your next data-driven success together! 📬 Contact Me Today to discuss how I can add value to your projects.

I build end-to-end data platforms — from raw source ingestion through dbt transformation to production dashboards — on Snowflake, BigQuery, Databricks, Sigma Computing, and Looker. One consultant who owns the full stack: ETL pipelines, cloud data warehouse, data modeling, and the BI reporting layer your team actually trusts. Founder of Warehows Analytics. Official partner with Snowflake, dbt, Databricks, and Sigma Computing. 32+ projects delivered across SaaS, e-commerce, fintech, healthtech, cybersecurity, and private equity. Top Rated Plus with 100% client satisfaction. $2.3M+ saved in client infrastructure costs. Stack: Snowflake, BigQuery, Databricks, dbt, Airflow, Fivetran, Airbyte, Hevo, Sigma Computing, Looker, Power BI, Superset, Streamlit, Python, SQL, FastAPI What I have delivered: - Fortune 500 — Oracle to Snowflake migration. 10M+ records/day CDC pipelines. Query times down 85%, costs down 60%. - $50M e-commerce brand — unified Google Ads, Facebook, email, and organic data. Single attribution model. ROAS from 3:1 to 7:1. - Finance SaaS (finsightsai.tech) — multi-tenant embedded analytics on Snowflake + Sigma serving 80+ customers. Sub-second queries. Built end to end: ingestion, dbt models, UI, LLM-powered insights. - Multi-brand e-commerce (4 brands) — Shopify, WooCommerce, QuickBooks, and ad platforms consolidated into Databricks + Looker. +65% marketing ROI. 80% less reporting time. - Cybersecurity startup — BigQuery warehouse consolidating Salesforce, RB2B, and product data. dbt-modeled attribution resolved three conflicting definitions of "converted lead." - Creative & PR agency — 50K+ customer reviews processed. AI sentiment analysis. Sigma dashboards. Client onboarding from 6 weeks to 1 week. - Veterinary clinic group — manual Excel to live cross-clinic dashboards. Airbyte + Airflow + dbt + Snowflake + Sigma. 10x faster onboarding. - Energy PE firm — Snowflake Cortex Analyst. Natural-language queries over sensitive portfolio data. - eMarketer — Snowflake reporting replacing 3-week manual Excel process. Runs daily, untouched. Services: - Cloud data warehouse design (Snowflake, BigQuery, Databricks) - ETL/ELT pipelines (Fivetran, Airbyte, Hevo, custom Python) - dbt transformation with testing, documentation, and semantic layer - BI dashboard development (Sigma, Looker, Power BI, Superset, Streamlit) - Embedded analytics for SaaS products (multi-tenant, row-level security) - Marketing attribution and revenue reporting - Data migration from legacy systems (Oracle, Talend, on-prem) - AI/LLM integration (RAG, conversational analytics, Cortex Analyst) Why this matters: data projects fail when nobody owns both ends. The pipeline engineer builds what was specified. The analyst reports what was delivered. Nobody checks whether either matches what the business needed. I hold the full picture — from raw source to executive dashboard — so the numbers your team sees are the numbers your finance team agreed to. Every model has tests. Every pipeline has monitoring. Every dashboard traces to a definition settled before the first chart was built. Send me a message. I respond the same day. Top Rated Plus · 32+ Projects · Snowflake Partner · dbt Partner · Databricks Partner · Sigma Computing Partner

 Highly Skilled IT Professional Experience in GCP and Azure Cloud with over 7+ years of experience working as GCP and Azure Data Engineer.  Overall, 16+ years in IT Experience. In Software Design, Development, Analysis, Testing, Data Warehouse and Business Intelligence tools.  Working within an Agile delivery methodology production implementation in iterative sprints GCP : BigQuery – Created external table on GCS Parque files, Views with Dedupe logic, Wrote the dynamic batch script to find the current/invalid parquet files in GCS 🖎 DataProc – Written in PySpark / Spark SQL program to transform data and used dataproc cluster to run the job. Implemented Delta lake 🖎 Composer, Apache Airflow – Used for workflow orchestrations. 🖎 Git – Used to maintain as a code repository 🖎 GCS – Storage used to keep processed data. Parquet/CSV files used to store data. 🖎 Programming – PySpark, Python, and Spark SQL used for script Cloud Data Platform (Azure):  Implemented standard Databricks Notebook used to load Full load and Delta load (Type 1) to process a large volume of data with all the business rules and transformation.  Used Scala and Spark SQL language to create standard Databricks Notebook  Implemented Azure Key Vault service to store all the vital credentials like Service Principal key, Database credentials, Storage connection string, and others  Knowledge of ADLA (U-SQL) replacement with Azure Databricks for data processing  Designed and implemented highly performant data ingestion ADF pipelines from multiple sources using Azure Databricks  Created some UDF for logging, History load from multiple date folders from data lake storage DW& Data Modeling:  Experience in OLTP/OLAP System Study, developing Database Schemas like Star Schema and Snowflake Schema used in relational, dimensional, and multidimensional modeling.  Experience in analysis, design, and construction of Data warehouses.  Expert Experience in Normalization, De-normalization.  Implemented Slowly Changing Dimensions - Type I & II in Dimension tables as per the requirements ETL:  Proficient in using SQL Server Integration Services to build Data Integration and Workflow Solutions, Extract, Transform and Load (ETL) solutions for Data warehousing applications.  Skilled in Business Intelligence tools like SQL Server 2008R2 Integration Services (SSIS).  Experience creating SSIS packages to automate the Import and Export of data to and from SQL Server 2008 using SSIS tools like Import and Export Wizard, Package Installation, and BIDS. T-SQL:  Extensive experience in using T-SQL (DML, DDL) in SQL Server 2012 / 2000 platforms.  Experienced in creating Tables, Stored Procedures, Views, Indexes, Cursors, Triggers, User Profiles, User Defined Functions, Relational Database Models, and Data Integrity in observing Business Rules.  Implemented Change Tracking (CT) and Change Data Capture (CDC) functionality  Extensive knowledge in tuning T-SQL, Query Optimization to improve the Stored Procedures/Functions performance and availability.

🔹 I build data platforms that handle petabytes of data, millions of events, and ensure zero-tolerance downtime. I’m a senior data engineer and cloud architect with over a decade of experience building production-grade, petabyte-scale data platforms for high-growth, data-intensive businesses. I specialize in real-time analytics, low-latency systems, and cloud cost optimization — the kind of work that breaks if done wrong and gets noticed by leadership when done right. 🚀 Proven Business Impact - +18% revenue uplift by detecting and blocking high-risk fraud users in a real-time data platform - Less than 30ms read/write latency for high-DAU applications handling millions of events per day - Production-grade pipelines supporting analytics & ML with strict data quality guarantees What I’m Typically Brought In For - Stabilizing broken or unreliable data pipelines - Designing real-time or near-real-time architectures - Reducing Snowflake/cloud warehouse costs Core Stack: - Cloud & Warehousing: AWS, GCP, Snowflake, Databricks - Data Engineering: Spark, Kafka, Airflow, dbt, Terraform - Real-Time Systems: Kafka, Flink, DynamoDB - Architectures: Data Lakes, Warehouses, Lakehouses How I Work I ship systems that survive production, scale, and audits. That means clear scope, measurable outcomes, and no “hope it works” engineering. 📌 If your data platform is a bottleneck or a liability, let’s fix it properly. Message me to discuss your system and constraints.