Hi, I’m Harish Padmanabhan — a Data Engineer, Machine Learning Engineer, and aspiring AI Engineer. As an M.S. Data Analytics Engineering student at Northeastern University (’26), I design scalable data architectures, develop machine learning models, and integrate AI-driven solutions into production. My toolkit includes Airflow, AWS, SQL/NoSQL, and Python ML frameworks. I’m passionate about building pipelines, predictive systems, and AI applications that deliver measurable results.
M.S. in Data Analytics Engineering (GPA: 3.81/4.00)
Coursework: Foundations of Data Analytics, Data Management for Analytics, Computation & Visualization, Data Mining in Engineering, Large Language Models.
Built batch & streaming data pipelines with Airflow/AWS Glue and modeled data in PostgreSQL/Redshift.
Applied ML for NL2SQL, predictive analytics, and dashboarding in Python (Pandas, scikit‑learn) and Tableau/Power BI.
B.E. in Mechanical Engineering (GPA: 8.21/10.00)
Coursework: Python Programming, Statistics & Numerical Methods; foundations in data analysis and computation.
Hands‑on projects emphasizing problem solving, quantitative analysis, and technical communication.
Transitioned interests toward data engineering and ML applications through electives and projects.
Infosys Limited
Built scalable data pipelines with Apache Airflow, AWS Glue, and S3—processing 200M+ records monthly with 40% lower ingestion latency.
Optimized SQL models in PostgreSQL/Redshift with indexing and partitioning, reducing query time by 55%.
Developed Tableau dashboards integrated with ETL workflows, improving decision-making speed by 25%.
Automated anomaly detection workflows with PySpark and AWS Lambda, reducing manual validation time by 70%.
Infosys Limited
Enhanced and maintained extbf{ETL pipelines} handling lease, asset, and payment data from financial systems, eliminating manual Excel errors and cutting data latency by 70%
Developed Python automation scripts to process multi-format API data (CSV, JSON, XML) into PostgreSQL, reducing upload time from 4 hours to 15 minutes.
Implemented data validation pipelines with Pandas and Great Expectations, ensuring 98% accuracy before production loads.
