Cloud Data Engineering & Backend Services
Designs scalable batch and ETL pipelines using Python, SQL, Spark,
Airflow, Databricks, and cloud-native services across GCP, AWS, and
Azure. Builds reliable data workflows optimized for performance,
scalability, quality, and enterprise analytics.
Enterprise Data Platforms
Architects modern data platforms using BigQuery, Snowflake, Dataproc,
Hadoop, and distributed processing technologies. Develops reusable
frameworks that support data integration, transformation, orchestration,
and self-service access across enterprise systems.
Streaming & Real-Time Data
Builds event-driven and real-time data pipelines using Kafka, Pub/Sub,
Flink, Spark Streaming, and Change Data Capture. Enables dependable,
low-latency data movement for operational systems, analytics platforms,
and business-critical applications.
AI, Machine Learning & LLMs
Develops production-ready machine learning pipelines, feature engineering workflows, and AI applications using TensorFlow, PyTorch, Scikit-learn, OpenAI APIs, LangChain, vector databases, embeddings, and Retrieval-Augmented Generation (RAG). Supports model deployment, inference, and intelligent automation.
DataOps & Platform Automation
Automates enterprise data platforms through CI/CD, Infrastructure as Code, Docker, Kubernetes, monitoring, orchestration, metadata automation, testing, and platform engineering. Improves reliability, scalability, governance, and operational excellence across cloud-native data ecosystems.
Analytics Engineering & BI
Builds semantic data models, analytical datasets, KPIs, executive dashboards, and self-service reporting solutions using SQL, Power BI, Tableau, Looker, and dimensional modeling. Enables trusted analytics for business users and leadership through scalable reporting architectures.
