Senior Data Engineer with deep expertise in designing and implementing enterprise-scale data platforms across cloud ecosystems. Specialized in building end-to-end data solutions that combine robust architecture, operational excellence, and business impact.
Core Competencies:
- 🏗️ Data Platform Architecture – Designing scalable, resilient data systems from concept to production
- ☁️ Multi-Cloud Expertise – Azure (ADF, Synapse), AWS (EMR, Glue), Snowflake data warehousing
- 🔄 Analytics Engineering – dbt-powered data transformation with testing, documentation, and governance
- ⚙️ Orchestration & Automation – Dagster, Apache Airflow for complex, monitored workflows
- 📊 Data Modeling & Semantics – Dimensional modeling, semantic layers, agentic analytics applications
- 🚀 Data Product Development – Building self-serve analytics, BI platforms, and intelligent data products
Philosophy: Build data systems that are not just functional, but observable, testable, documented, and accessible to every stakeholder.
- ✅ Architected multi-cloud data pipelines across Azure and AWS, handling petabyte-scale datasets
- ✅ Implemented production-grade dbt projects with 90%+ test coverage and comprehensive documentation
- ✅ Designed semantic layers enabling non-technical stakeholders to perform self-serve analytics
- ✅ Built agentic analytics systems combining intelligent data retrieval with automated insights generation
- ✅ Optimized data pipeline performance, reducing end-to-end latency by 40%+ through intelligent partitioning and incremental loading
- ✅ Managed Azure Data Factory deployments with 1000+ daily pipeline executions
- ✅ Orchestrated Databricks & Apache Spark clusters for distributed analytics at scale
- ✅ Configured Snowflake warehouses with cost-optimization and performance tuning
- ✅ Implemented Infrastructure-as-Code practices for reproducible, version-controlled deployments
- ✅ Developed advertising analytics frameworks supporting DSP, ad server, and CRM data integration
- ✅ Created cross-database compatible dbt macros for warehouse portability (BigQuery, Snowflake, Redshift, Postgres)
- ✅ Implemented data snapshots & slowly-changing dimensions for dimensional analytics
- ✅ Built source-agnostic data models for Stripe, HubSpot, and Shopify commerce platforms
Python ████████████████░░ Proficient
SQL ████████████████░░ Expert
Bash ███████████░░░░░░░ Intermediate
dbt ████████████████░░ Expert
Dagster ███████████████░░░ Advanced
Apache Spark ███████████████░░░ Advanced
Apache Airflow ██████████░░░░░░░░ Intermediate
PySpark ███████████████░░░ Advanced
Azure Data Factory ████████████████░░ Expert
Azure Synapse ██████████████░░░░ Advanced
AWS EMR / Glue ███████████░░░░░░░ Intermediate
Snowflake ███████████░░░░░░░ Intermediate
PostgreSQL ██████████░░░░░░░░ Intermediate
Docker ████████████░░░░░░ Advanced
Git / GitHub ████████████░░░░░░ Advanced
Linux ███████████░░░░░░░ Intermediate
Jupyter ████████████░░░░░░ Advanced
| Project | Description | Tech Stack | Impact |
|---|---|---|---|
| Azure Data Factory Pipelines | Production-grade orchestration workflows managing enterprise data movement across systems | Azure Data Factory, Linked Services, Triggers | 1000+ daily executions |
| NEXUS Data Platform | Unified data platform for multi-source analytics | Python, ADF, Synapse | Enterprise integration |
| Azure Synapse Analytics | End-to-end analytics solution on Azure Synapse with optimized data modeling | T-SQL, Synapse, Delta Lake | Real-time dashboards |
| Project | Description | Tech Stack | Key Features |
|---|---|---|---|
| Advertising Analytics dbt | Production-ready dimensional models for digital advertising ecosystem | dbt, SQL, Jinja2 | 50+ models, 100+ tests, snapshots, macros |
| dbt Analytics Utils | Reusable, cross-warehouse dbt macro library for enterprise deployments | dbt, SQL | BigQuery, Snowflake, Redshift, Postgres compatible |
| dbt Stripe | SaaS payment analytics data models | dbt, Stripe API models | Fully tested & documented |
| dbt HubSpot | CRM-focused dimensional models for marketing analytics | dbt, SQL, YAML | Complete entity relationships |
| dbt on Snowflake | Best-practices starter project for data teams | dbt, Snowflake, CI/CD | Production-ready template |
| Project | Description | Tech Stack | Innovation |
|---|---|---|---|
| Shopify Orchestration | Dagster-based orchestration for e-commerce data pipelines with monitoring & observability | Dagster, Python, PostgreSQL | Asset-based DAG, automated backfills, alerts |
| Dagster Pipeline Framework | Reusable orchestration patterns for complex data workflows | Dagster, Python | Modular asset definitions, dynamic parallelization |
| Semantic Analytics Agent | Agentic analytics system with semantic layer for autonomous insights | Python, LLM, Semantic Models | Self-serve analytics, natural language queries |
| Project | Description | Tech Stack | Scale |
|---|---|---|---|
| AWS EMR Project | Distributed analytics on Hadoop/Spark clusters | AWS EMR, PySpark, Jupyter | Terabyte-scale processing |
| PySpark Data | Advanced PySpark patterns for distributed computing | PySpark, Delta Lake | Optimized joins & aggregations |
| E-commerce Data Analysis | Business analytics using SQL for retail datasets | SQL, Data Modeling | Cohort analysis, RFM segmentation |
| IPL Final 2024 Analysis | Statistical sports analytics with advanced visualizations | Python, Pandas, Matplotlib | Predictive modeling |
| Project | Description |
|---|---|
| Data Engineering Books | Curated learning resources and reference materials for data professionals |
- Multi-cloud platform design (Azure, AWS, Snowflake)
- Dimensional modeling (Kimball, Star Schema)
- Data mesh & federated analytics architectures
- Real-time vs. batch processing trade-offs
- Data warehouse and lakehouse design patterns
- End-to-end dbt project governance and best practices
- Data testing frameworks (dbt tests, Great Expectations)
- Semantic layers and business intelligence
- Data documentation and metadata management
- Version control and CI/CD for analytics code
- Complex DAG design and optimization
- Event-driven and scheduled workflows
- Error handling, retry logic, and alerting
- Data lineage and dependency tracking
- Observability and monitoring
- Azure: Data Factory, Synapse, Databricks, ADLS
- AWS: EMR, Glue, S3, Redshift, Lambda
- Snowflake: Warehouse architecture, cost optimization, roles & permissions
- Multi-source data consolidation
- Real-time streaming (Kafka, Event Hubs)
- Change Data Capture (CDC) patterns
- API integration and webhook handling
- Data quality and validation frameworks
- Digital advertising & marketing analytics
- SaaS & subscription economics
- E-commerce and retail analytics
- CRM and customer analytics
- Financial and operational reporting
✅ Production-Ready Code – All projects follow enterprise standards
✅ Fully Documented – Comprehensive README, docstrings, YAML documentation
✅ Well Tested – Unit tests, integration tests, data quality checks
✅ Version Controlled – Clean git history, semantic versioning
✅ Observable Systems – Logging, monitoring, and alerting built-in
I'm actively engaged with the data engineering community and open to collaborating on:
- 💡 Enterprise data platform transformations
- 📈 Analytics engineering best practices
- 🔬 Data innovation and emerging technologies
- 👥 Mentoring data engineers and analysts
- 🌐 Speaking engagements and thought leadership
Building the future of data-driven enterprises, one pipeline at a time.
Last updated: 2026 | Always learning, always building 🚀