pralakxavier
Remote: Yes, open to remote opportunities
Willing to relocate: Yes, within the USA
Technologies: Python, SQL, Snowflake, dbt, PySpark, Pandas, Apache Airflow, AWS, Snowpipe, Great Expectations, pytest, Terraform, GitHub Actions, Power BI
Résumé/CV: [Download Resume](https://drive.google.com/drive/folders/1CVF5ANe8fCR112NKv1tZ...)
Email: [email protected]
I’m a Data Engineer with 4+ years of experience building data pipelines, working with cloud data platforms, and turning raw data into reliable, analytics-ready datasets. My main areas of expertise are Python, SQL, Snowflake, dbt, PySpark, Apache Airflow, and AWS. I enjoy solving data processing challenges, improving performance, and making pipelines more reliable and easier to maintain.
Some of the work I’ve done includes:
Snowflake Optimization: Improved query and pipeline runtimes by 30–40% across 100+ SQL models through query tuning, clustering, materialized views, and join optimization.
Data Modeling: Built and maintained 100+ dbt models across bronze, silver, and gold layers, with automated testing and code reviews to maintain data quality.
Data Ingestion: Developed CDC pipelines using Snowpipe and Snowflake Streams to automate data ingestion from 12+ source environments and reduce manual work.
Data Quality: Reduced recurring data defects by 30–36% by adding validation checks using Python, Great Expectations, and pytest to catch schema changes, missing values, and data anomalies early.
Machine Learning Data: Prepared and validated feature and training datasets using Python and Snowflake to support ML workflows with MLflow and Amazon SageMaker.
Big Data Processing: Built PySpark and Pandas pipelines to process data from 20+ different sources, improving processing runtimes by 30–40% through join and partition optimization.
Pipeline Automation: Developed event-driven workflows with Apache Airflow, improving data delivery from multi-hour processing cycles toward near-real-time availability for 200+ downstream stakeholders.
Cloud Infrastructure: Supported the migration of legacy ETL workloads to AWS and used Terraform to manage infrastructure across development, testing, and production environments.
My experience covers the full data engineering lifecycle, from ingestion and transformation to data modeling, quality checks, orchestration, and performance tuning. I’m particularly interested in building scalable data solutions that make data more accessible, consistent, and useful for business and analytics teams.
I’m currently exploring Data Engineer and Senior Data Engineer opportunities where I can apply my experience, contribute to meaningful projects, and continue growing technically. I’m open to remote, hybrid, and onsite roles, and I’m willing to relocate within the USA.