Hello! I'm

Uppara Uday Sagar

Data Analyst Data Engineer ServiceNow

Data Analyst skilled in transforming complex datasets into actionable insights using SQL, Python, and machine learning. Experienced in building predictive models, ETL pipelines, and interactive dashboards to drive data-driven decision-making and product improvements.

3+ Analytics and platform automation projects
25-30% Client-side load reduced during internship work
2 ServiceNow certifications with hands-on workflow building

Data work with product instincts, ServiceNow automation, and clean execution.

I'm a Computer Science (Data Science) graduate who enjoys building reliable systems around analytics, not just models in isolation. My work spans data cleaning, exploratory analysis, machine learning pipelines, dashboards, and platform workflows that make operations easier to run.

During my internship at INCOIS, I improved SARAT(Search And Rescue Tool) workflows by moving heavy logic into the backend, refining interface behavior, and supporting faster rescue-focused operations. I also build ServiceNow process automations that connect approvals, asset workflows, SLA tracking, and operational visibility.

Languages

Python, SQL, Java

Strong foundations for data wrangling, querying, backend logic, and applied problem solving.

Analytics

EDA, preprocessing, KPI analysis

Comfortable shaping raw data into decision-ready metrics, patterns, and clear narratives.

Libraries

Pandas, NumPy, Scikit-learn, Streamlit

Hands-on experience building practical model pipelines and interactive data applications.

Tools

Power BI, Tableau, Excel, Git, Jupyter, ServiceNow

From reporting to workflow automation and versioned delivery, I like systems that stay reliable and presentation-ready.

Projects built to predict behavior, explain patterns, automate workflows, and support better decisions.

India Air Quality Intelligence Dashboard showing AQI KPI cards, filters, and pollution insights Data Analytics Dashboard

Project-01

India Air Quality Intelligence Dashboard

An interactive dashboard that turns Government of India AQI records into a decision view for identifying pollution hotspots, pollutant patterns, and breach severity across states and cities.

  • Problem solved: Raw AQI records were difficult to compare across locations, monitoring stations, pollutants, and time-based severity levels.
  • Analysis performed: Cleaned missing pollutant rows, normalized location fields, ranked states and cities, classified AQI breaches, and compared pollutant-level contributions.
  • Insights found: The dashboard highlights top polluted cities, recurring geographic hotspots, severe breach zones, and pollutants driving poor air quality.
  • Why SQL mattered: SQL logic handled grouping, filtering, breach classification, and ranking so the dashboard shows reliable insights instead of surface-level charts.
Python SQLite SQL Plotly Streamlit
E-shram Workforce Analytics Dashboard overview in Power BI Business Intelligence

Project-02

E-shram Workforce Analytics Dashboard

A workforce analytics dashboard that converts E-shram registration data into a clearer view of demographic, regional, and occupation-level labor patterns.

  • Problem solved: Workforce records needed to be organized into decision-ready segments for understanding who is registered, where they are located, and what work categories dominate.
  • Analysis performed: Built ETL steps, structured reporting tables, and analyzed occupation, age, gender, and regional distribution across interactive Power BI views.
  • Insights found: The dashboard makes it easier to spot concentrated worker groups, demographic skews, high-volume regions, and occupation trends that can guide planning.
  • Why SQL mattered: SQL-backed transformations created consistent categories and clean aggregations, reducing dashboard ambiguity and making comparisons trustworthy.
Power BI SQL ETL

Workflow automation, approvals, SLA monitoring, and platform dashboards.

ServiceNow asset auto allocation workflow and SLA dashboard ServiceNow Automation

Project-03

Asset Auto Allocation & SLA Tracking (ServiceNow)

A ServiceNow operations focused on reducing manual asset assignment delays while improving approval visibility, SLA tracking, and fulfillment accountability.

Problem Solved

  • Manual allocation slowed request fulfillment.
  • Approval delays were hard to trace.
  • SLA risk was not visible early enough.
  • Teams lacked a single operational dashboard.

Analysis Performed

  • Mapped request flow from catalog item to RITM.
  • Defined approval and allocation decision points.
  • Tracked SLA lifecycle using task_sla records.
  • Measured fulfillment status through Platform Analytics.

The key insight was that fulfillment performance depends on both asset availability and process timing. Flow Designer logic mattered because it connected approvals, availability checks, allocation actions, and SLA events into one traceable workflow instead of leaving them as disconnected manual steps.

ServiceNow Flow Designer SLA Tracking Platform Analytics

Hands-on engineering experience in a real-world operational environment.

Summer Intern

INCOIS(Indian National Centre for Ocean Information Services)

April 2025 to June 2025 • Hyderabad

  • Enhanced SARAT backend performance using Django to support faster rescue-related workflows.
  • Refined frontend components to improve usability and real-time information clarity.
  • Shifted JavaScript-heavy logic to Django and Python, reducing client-side load by about 25-30%.
  • Collaborated on geospatial calculation and system reliability improvements.
Django Python JavaScript MySQL Apache

A data science foundation backed by strong academic momentum.

2022 - 2026

Vignana Bharathi Institute of Technology

B.Tech in Computer Science (Data Science)

CGPA: 7.34
2020 - 2022

Narayana Junior College, Hyderabad

Intermediate MPC

Percentage: 90%

Let's build analytics experiences that people can trust.

If you need someone who can move between data preparation, modeling, dashboards, ServiceNow workflows, and product-minded delivery, I'd love to connect.