Salman K.

About Salman K.

Over the past 5 years, I have specialised in bridging the gap between data engineering and data science. My focus is on building and optimising the large scale pipelines that turn raw data into meaningful insights. I have used Python, SQL, and PySpark or building ETL pipelines and workflows across AWS and Azure to prioritise creating stable, high-performance systems. I enjoy translating and brainstorming on complex business requirements into a workflow with multiple tasks, deriving insights from the data. By overseeing the end-to-end data lifecycle, I help team improve data quality and reduce processing time. My approach combines the skills of engineering with a deep understanding of the analytical workflow, ensuring that the data ecosystems are both scalable and sustainable for the long term.My experience as Data Engineer gave me the opportunity to work closely with data and analytics which peaked my interest and inspired me to explore and wanting to transition into Data Science path. I have been working sharpening my skillset for Data Scientist roles with the help of projects and courses. I have built multiple projects where I have integrated APIs or Gen AI capabilities. Worked with multiple AI tools and services. Transitioning to a data scientist role would give me an exciting opportunity to combine my technical expertise with my passion for analytics and problem-solving.Looking for an opportunity to sharpen my skills through hands-on projects and collaborative work.Skills Languages - Python, SQL, PySpark.Data analysis and platforms- Databricks, AWS EMR, Lambda, Step Functions, EventBridge, CloudWatch, Jupyter, Dremio, Azure functions, BigQuery, Redshift, Swagger, Prophecy, Jira, Excel.

Experience

1. Senior Data Scientist

   Nielsen

   Jun 2025 — Present · Mumbai, IN

   Skills - Python, SQL, PostgreSQL, PySpark, AWS Lambda, Statistical modelling, Jupyter.• Built an end to end data pipeline with AWS services that includes EMR, Step Functions, Glue, Athena, EventBridge, CloudWatch and QuickSight, reducing the manual effort by 95%. • Optimised scripts and ran analysis for audience measurement on campaign data with more than 500M records.• Automated the process of calculating the Universe Estimate for the population which serves as a denominator for all the key KPIs and metrics, cutting down the timeline by 60%.• Brainstormed and formulated methodology to get valuable insights from user behaviour, engagement and mediaconsumption on CTV.

Education

• Royal Junior College

  HSC, Science

  2014 — 2016

• MGM college of Engineering and Technology

  Bachelor of Engineering - BE, Computer Engineering

  2016 — 2020