Data Engineer Intern Candidate (Batch & Streaming)

PARTH ALAGH

New Delhi, India • +91-870**-***** • ************@*****.*** • linkedin.com/in/parth-alagh-170606297 • github.com/notparthh0 • parthalagh.framer.website

PROFESSIONAL SUMMARY

Results-oriented Data Engineering student with hands-on experience building production-grade batch and real-time data pipelines on AWS and GCP. Skilled in distributed processing (PySpark, Kafka, Spark Streaming), cloud data infrastructure (S3, Athena, BigQuery), transformation frameworks (dbt, Airflow), and machine learning integration. Comfortable working across the full data stack from ingestion to model serving. EDUCATION

University School of Information, Communication and Technology (USICT) New Delhi, India B.Tech in Information Technology CGPA: 8.55/10.0 Expected: May 2027

● Relevant Coursework: Database Management Systems, Data Structures & Algorithms, Statistical Analysis, Machine Learning, Operating Systems, Probability & Statistics, Linear Algebra

● Achievement: Ranked in Top 1% nationwide in CBSE Class XII (93.4%) TECHNICAL SKILLS

Data Engineering & Cloud: ETL/ELT Pipelines, Batch & Stream Processing, AWS (S3, Athena, Glue), GCP (BigQuery, Cloud Storage, Dataflow), Apache Spark, Apache Kafka, Spark Structured Streaming, Docker, Kubernetes, Terraform, dbt, Apache Airflow Languages: Python (Pandas, NumPy, Scikit-learn), SQL (Advanced), Bash/Shell Scripting, YAML Machine Learning & AI: XGBoost, Time-Series Forecasting, LLMs, Gemini API, OpenAI API, LangChain, Agentic AI, RAG, NLP, Prompt Engineering Databases: PostgreSQL, MySQL, BigQuery, AWS Athena Analytics & Visualization: Streamlit, Power BI (DAX, Data Modeling), Excel (Power Query, VBA), Jupyter Notebooks Tools & Methodologies: Git, GitHub, GitHub Actions (CI/CD), REST APIs, JIRA, Agile/Scrum, Linux EXPERIENCE

Data Engineering Intern – Xtelify Ltd (Airtel Group) July 2026 – Sept 2026 Hybrid — Airtel Center, Udyog Vihar, Gurgaon

● Developed Kafka Streams consumer pipelines for Airtel's OTP transactional messaging system, handling windowed aggregations, KStream/KTable joins, and exactly-once delivery guarantees across high-volume telecom event streams.

● Implemented stateful stream processing logic for real-time message deduplication and session tracking using the Kafka Streams DSL.

● Containerised pipeline services using Docker Compose; studied Kubernetes deployment patterns for scaling distributed messaging workloads in production.

● Navigated a production-grade distributed backend codebase processing millions of OTP events daily, gaining exposure to system design decisions at telecom scale.

● Contributed to active pipeline and infrastructure improvement tasks alongside senior engineering team members. Project Intern – Agentic AI IBM SkillsBuild / CSRBOX July 2025 – Aug 2025

● Built and deployed autonomous AI agents using Python and LLM APIs, transitioning prototypes into functional multi-step agentic workflows in an Agile team.

● Engineered scalable AI workflows integrating LangChain APIs, cutting manual task execution by 60% through automated pipeline orchestration on GCP.

KEY PROJECTS

NYC Taxi Batch ELT Pipeline Python, PySpark, AWS S3, AWS Athena, dbt, XGBoost, Streamlit, Apache Airflow

● Built an end-to-end batch ELT pipeline processing 3,066,766 NYC Yellow Taxi trip records with PySpark; cleaning rules reduced noise by 6%, yielding 2,881,179 validated records stored as Parquet on AWS S3.

● Queried data serverlessly via AWS Athena, built a two-layer dbt transformation (staging + hourly demand mart), and orchestrated monthly runs with an Apache Airflow DAG.

● Trained an XGBoost demand forecasting model with strict temporal holdout validation (R =0.91, RMSE=21.63 trips, 70.1% better than mean baseline); deployed a live Streamlit dashboard querying Athena in real time. Real-Time Streaming Analytics Pipeline Python, Apache Kafka, Spark Structured Streaming, Gemini API, Streamlit

● Building a real-time urban mobility analytics system that simulates NYC taxi trip streams through Apache Kafka.

● Processing streams with Spark Structured Streaming for windowed demand aggregation per zone.

● Integrating Gemini API to generate natural language explanations for detected demand anomalies (e.g., unusual surge at JFK at 11PM); serves as the engineering foundation for a research paper on LLM-augmented anomaly detection in urban data streams. CDC + Lakehouse Pipeline PostgreSQL, Debezium, Kafka Connect, Terraform, GCP

● Building a change-data-capture pipeline with Debezium and Kafka Connect to stream row-level PostgreSQL changes.

● Landing changes in an Iceberg lakehouse on GCS, with tables exposed to BigQuery as external tables.

● Provisioning all cloud infrastructure as code with Terraform. CERTIFICATIONS

● IBM Data Engineering Professional Certificate — Coursera

● Google Cloud Certification: Cloud Data Engineer — Coursework Completed

● Applied Data Science with Python — University of Michigan

● Machine Learning Specialization — DeepLearning.AI (Andrew Ng)

● Microsoft Power BI Data Analyst

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