I provide end-to-end Data Engineering solutions that help organizations build, optimize, and scale modern data platforms. From batch pipelines to real-time streaming systems, I design data infrastructure that is reliable, performant, and analytics-ready.
What I Can Help You With:
🔹 ETL / ELT Pipeline Design
Design and implement robust ETL/ELT pipelines
Batch and incremental data ingestion
Schema evolution and pipeline orchestration
Optimized for analytics, ML, and reporting workloads
🔹 Apache Spark & Databricks
Distributed data processing using Apache Spark
Data transformation and optimization in Databricks
Performance tuning and cost optimization
Large-scale data processing and feature engineering
🔹 Data Warehousing
Design and implementation of cloud data warehouses:
Amazon Redshift
Google BigQuery
Snowflake
Dimensional modeling (Star/Snowflake schemas)
Query optimization and cost-efficient storage strategies
🔹 Real-Time Data Processing
Streaming pipelines using Apache Kafka
Workflow orchestration with Apache Airflow
Near real-time analytics and event-driven architectures
Fault-tolerant and scalable pipeline design
🔹 Data Quality Management
Data validation and anomaly detection
Data completeness, consistency, and accuracy checks
Automated monitoring and alerting
Trustworthy, production-grade datasets
🔹 Data Lake Architecture
Cloud-based data lake design (S3 / GCS / ADLS)
Bronze–Silver–Gold data layering
Structured, semi-structured, and unstructured data handling
Governance-ready architectures
🔹 SQL & Database Optimization
Advanced SQL query optimization
Indexing and partitioning strategies
Performance tuning for high-volume datasets
Cost and latency reduction
Tools & Technologies
What You Get
✅ Clean, well-documented pipelines
✅ Scalable and maintainable architecture
✅ Performance-optimized data systems
✅ Production-ready solutions aligned with business goals