Skills & Expertise

Python

Pandas, NumPy, scikit-learn, TensorFlow, and PySpark workflows

PostgreSQL

Schema design, indexing, query optimization, and analytics modeling

R

Statistical analysis, hypothesis testing, and predictive modeling

Jupyter

Notebook-based analysis, experimentation, and model prototyping

NumPy

Vectorized computation, numerical methods, and performant transforms

Pandas

Data wrangling, feature engineering, and time-series preparation

Apache Airflow

DAG orchestration, dependency management, and scheduled workflows

Apache Spark

Distributed compute, large-scale transforms, and ETL acceleration

Databricks

Lakehouse workflows, notebook collaboration, and production jobs

Docker

Containerized services, reproducible environments, and local parity

Terraform

Infrastructure as code for cloud provisioning and environment control

GitHub Actions

CI/CD pipelines, automated checks, and deployment workflows

AWS

Redshift, Glue, Lambda, and S3 data platform architecture

Azure

Analytics services, storage integration, and enterprise deployment

Google Cloud

Managed analytics services and scalable cloud-native workloads

Snowflake

Warehouse modeling, secure sharing, and performance tuning

BigQuery

Serverless analytics, partitioned tables, and high-volume SQL

Data Warehousing

Dimensional modeling, governance standards, and data quality controls

scikit-learn

Feature pipelines, model validation, and classical ML productionization

TensorFlow

Neural networks, deep learning experiments, and model iteration

PyTorch

Model prototyping, experimentation, and GPU-enabled training

Power BI

DAX modeling, semantic layers, and executive-ready reporting

Looker

LookML modeling, governed explores, and self-serve analytics

Microsoft Excel

Advanced formulas, pivots, scenario analysis, and stakeholder delivery