Rajnish Jangu

Engineer — Data & Operations Analytics

Hisar, Haryana $4/hr Hybrid Full-time 1+ yrs exp 88 · Excellent

About

NIT engineering graduate with 1.5 years of analytical experience and hands-on expertise in SQL, Python, ETL, data warehousing, data modeling, Power BI, and business analytics. Built end-to-end data solutions involving data preparation, validation, dimensional modeling, KPI analysis, and reporting, transforming enterprise data into reliable, analytics-ready datasets and business insights.

Skills & Expertise (67)

Data Analysis Intermediate

7.0/10

1.5

Years Exp

Data Cleaning Intermediate

6.5/10

1.5

Years Exp

SQL Intermediate

6.5/10

1.5

Years Exp

ETL Intermediate

6.5/10

1.5

Years Exp

Python Intermediate

6.0/10

1.5

Years Exp

Window Functions NumPy Indexing Views Subqueries Stored Procedures Aggregations EDA CTEs Joins T-SQL Git/GiHub Analytical & Communication Skills Analytical Thinking & Troubleshooting REST APIs Strong Analytical & Problem-Solving Skills Data Validation Trend Analysis Anomaly Detection Root Cause Analysis ELT Power BI Power Query DAX Advanced Excel SQL Server Management Studio SSMS Git GitHub MS Excel (Pivots, VLOOKUP/XLOOKUP, Power Query) A/B testing Statistical Analysis Trends Analysis KPI Analysis Data Validation & Reporting Data Cleaning & Transformation Exploratory Data Analysis (EDA) Power BI (DAX, Dashboards, Data Modeling) Root Cause Analysis & Troubleshooting Microsoft Excel (Advance) SQL Server (SSMS) Snowflake NumPy library Pandas library SQL (T-SQL) Programming Languages: Python Collaborative Problem-Solving Anomaly analysis Data Visualization Tableau (Basic knowledge) Business Analytics Operational Analytics Business Reporting ETL (Extract transform load) Data Pipelines Data Transformation Data Warehousing Data Modeling Dimensional Modelling Star Schema Medallion Architecture Pandas

Work Experience

Engineer — Data & Operations Analytics

IOL Chemicals & Pharmaceuticals Ltd.

Jul 2024 - Nov 2025

Analyzed manufacturing data across 500+ API production batches to monitor process KPIs, yield, performance, and production efficiency. Conducted controlled A/B testing of Levetiracetam process conditions, analyzing batch-level phase-separation performance to identify the optimal operating approach and achieve a 24% reduction in acidic-layer separation time, from approximately 18–20 hours to 12–15 hours. Automated the month-end production reporting workflow using Power Query and Power BI, transforming recurring Excel production data into standardized KPI datasets and interactive dashboards, reducing report preparation time from approximately 1 working day to 2–3 hours (69%). Monitored production and solvent recovery data to identify process losses, contributing to an improvement in ethyl acetate and MDC solvent recovery efficiency from 92% to 94%.