Data Engineering | Cloud Analytics | Time Series Forecasting
I am a seasoned data engineering professional with more than a decade of experience designing robust, scalable, and cloud-native data ecosystems that transform raw data into strategic insights.

• I craft and orchestrate end-to-end ETL/ELT pipelines using Python, SQL, and Alteryx.
• I build cloud-native data architectures on AWS and Databricks, with infrastructure automation via Terraform.
• I create business-focused dashboards using Tableau, while ensuring observability and operational excellence with Datadog and Grafana.
• My current areas of interest include Time Series Forecasting, MLOps for production-grade models, and data engineering automation.

• Languages & Frameworks: Python, SQL, PySpark, Airflow
• Cloud & Infrastructure: AWS (S3, Glue, RDS, Redshift), Databricks, Terraform, Docker
• Data Stores: Oracle, AWS (Redshift, RDS, S3 (Parquet/CSV))
• BI & Visualisation: Alteryx, Tableau, Jupyter Notebooks
• Additional Expertise: CI/CD, observability, unit testing for resilient data pipelines

• python-learning - Introduction Python for Data Engineers.
• aws_boto3_learning - Introduction to AWS Boto3 in Python.
• terraform-aws — Terraform modules and templates for AWS infrastructure deployment.
• data-engineering-project — End-to-end data pipeline architectures and best-practice implementations.
• airflow-learning — Airflow DAG examples and operational workflows for orchestrating ETL pipelines.
• time-series-forecasting — Jupyter notebooks showcasing forecasting techniques and model evaluation.

• I prioritise data integrity, idempotent design, and end-to-end observability.
• I focus on writing clean, maintainable, and production-friendly code that scales effortlessly.
• My repositories follow an organised structure for clarity—README, documentation, examples, and unit tests.

• LinkedIn: https://www.linkedin.com/in/lakshmipriyanka-k
• Email: priyanka.kaduluri@gmail.com