Headline

Azure Data Engineer · Databricks · ADF · PySpark · Delta Lake · ETL/ELT Pipelines at Unisys India

About

Azure Data Engineer · Databricks · ADF · PySpark · Delta Lake · ETL/ELT Pipelines at Unisys India I've automated away 2–3 days of manual pipeline work — not once, but across every client onboarding cycle at Unisys. That's the kind of leverage I look for in every problem I tackle. I'm a Data Engineer with 14 months of production experience (6-month internship + 8 months full-time) building cloud-native data pipelines on Azure. My stack: Databricks, Azure Data Factory, PySpark, Apache Airflow, Delta Lake, and dbt. I specialise in Medallion Architecture (Bronze → Silver → Gold) and have migrated 15+ IICS and PowerCenter mappings to PySpark, hitting 100% data accuracy validation before every production cutover. A few things I've shipped: a Python automation tool that generates ProcessControl INSERT scripts directly from mapping sheets (now adopted across multiple client onboarding cycles); a reusable Databricks Asset Bundle template that became the team's default scaffolding; and a reconciliation framework that validates Databricks outputs against legacy ETL results at scale. I'm currently contributing to a Databricks workspace migration at Unisys and actively looking for Data Engineer roles where I can work on complex pipelines at scale. If that sounds like your team, let's talk.

Location

India

Work experience

Associate Data Engineer at Unisys, Oct 2025 - Present · 8 mos, Eliminated 2–3 days of manual pipeline development per client by engineering a Python tool that auto-generates ProcessControl INSERT scripts from mapping sheets — now the standard across all new onboarding cycles. Reduced new pipeline setup time team-wide by designing a reusable Databricks Asset Bundle (DAB) template for Silver, Gold, and SLA load patterns — adopted as the default scaffold for all client pipelines. Maintaining full business logic parity while migrating Genesys and FSM pipelines from the legacy to the new Databricks workspace, minimising production risk across a live enterprise environment.

Junior Data Engineer at Unisys, Mar 2025 - Sep 2025 · 7 mos, Reduced manual ETL intervention to zero by automating end-to-end data ingestion on Kubernetes — covering file validation and CSV-to-Delta Lake conversion for repeatable, scalable runs. Cut migration effort per pipeline by reverse-engineering 15+ IICS and PowerCenter mappings and re-implementing transformation logic in PySpark under Medallion Architecture (Silver → Staging → Gold). Achieved 100% data accuracy validation across all migrated pipelines by building a reconciliation framework comparing Databricks outputs against legacy IICS/PowerCenter results before every production cutover.

Developer Intern - AI at Infosys Springboard, May 2024 - Jul 2024 · 3 mos, Remote, Worked on Hate Speech Detection using Natural Language Processing (NLP) and Deep Learning, developing a robust classification model to identify harmful content. Preprocessed and cleaned large-scale text datasets, applied tokenization, stopword removal, and word embeddings (Word2Vec, TF-IDF, FastText) to improve feature representation. Designed and fine-tuned deep learning models (LSTMs, CNNs) to enhance classification accuracy, optimizing hyperparameters for better generalization. Conducted performance evaluation using precision, recall, F1-score, and ROC-AUC metrics, refining model architecture to reduce false positives and false negatives. Gained hands-on experience with TensorFlow, PyTorch, and Scikit-learn, learning end-to-end model development and evaluation techniques. Collaborated with mentors and peers, contributing to research discussions and technical presentations, enhancing problem-solving and analytical skills.

Education

Computer science engineering, Artificial Intelligence, Dayananda Sagar University

Bachelor, mahesh pu college