Sanskar Gupta

Data Analyst

Bengaluru, Karnataka, India2 yrs 4 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Reduced validation cycle time by 85% through automation.
Improved data accuracy from 65% to 95% with anomaly detection.
Enhanced user engagement by 20% with data-driven strategies.

Stackforce AI infers this person is a Data Analytics and Engineering professional with a focus on SaaS solutions.

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Skills

Core Skills

Data AnalyticsData EngineeringData Visualization

Other Skills

AJAXAWSAWS LambdaAgile EnvironmentAirflowAnalytical SkillsApache AirflowApache KafkaApache SparkArtificial Intelligence (AI)Azure Data FactoryBack-End Web DevelopmentBatch ProcessingBuild Strong RelationshipsC (Programming Language)

About

I’m passionate about turning raw data into actionable insights and building scalable data systems. With experience in data pipelines, analytics, and automation, I help organizations unlock the full potential of their data. Currently, I’m looking for opportunities in Data Analytics and Data Engineering where I can contribute to designing efficient workflows, creating impactful dashboards, and enabling data-driven decision making

Experience

2 yrs 4 mos

Total Experience

1 yr 2 mos

Average Tenure

1 yr 4 mos

Current Experience

Nielsen

Data Analyst 2

Feb 2025 – Present · 1 yr 4 mos · Bengaluru, Karnataka, India · Hybrid

Reduced validation cycle time by 85% in one month by automating household data validation workflows through case-wise filtering, structured inconsistency detection, and scheduled batch executions using AWS and SQL.
Improved reporting TAT by 25% in one month by engineering scalable reporting pipelines that automated scheduling, standardized monitoring, and ensured timely delivery to strengthen stakeholder confidence and adoption, via dashboards and KPI trackers, using Spotfire and Excel.
Improved data accuracy from 65% to 95% in one month by designing an anomaly cross‑validation framework that differentiated genuine low activity from technical failures, using RMS session checks, ETL pipelines and dashboards built using analytical and visualization tools like Spotfire and AWS.
Built scalable BI dashboards by converting raw household streaming, device telemetry, and cloud log data into structured datasets, KPIs, that enabled data visibility and analytical readiness using data analysis and visualization tools like Spotfire, Excel, and Power BI.
Enabled data-driven decision-making by converting complex analytical outputs into actionable insights and recommendations for leadership and operations teams, by performing root cause analysis and cross-system validation using RMS session data, AWS, Power BI and SQL.
Recognized by leadership for converting manual monitoring into a scalable analytics-driven workflow.

AWSSQLSpotfirePower BIData AnalysisData Visualization+2

Turing

Research Analyst

Oct 2024 – Feb 2025 · 4 mos · Palo Alto, California, United States · Remote

Enhanced user engagement by 20% in one quarter by executing data-driven strategies through cross-functional alignment, KPI tracking, and analytics-based decision-making; implemented modeling, automation, and dashboarding using Python, Spotfire, Power BI, and SQL.
Improved delivery TAT from 70% to 85% in one quarter for stakeholders, by designing modular workflow automation frameworks and eliminating redundant manual processes, through query automation, scripting and building an efficient dashboard using Spotfire, Python and SQL.
Improved customer retention by 26% in six months and enabled cross‑partner collaboration by coordinating onboarding initiatives across marketing partners and influencers and embedding structured training into product usage, through orchestration, query optimization, and efficient dashboarding using tools like Spotfire, AWS and SQL.

PythonSpotfirePower BISQLData Analytics

Cisco

Consulting Engineer

Oct 2022 – Oct 2023 · 1 yr · Bengaluru, Karnataka, India · On-site

Improved project efficiency from 70% to 85% in one quarter by collaborating with internal and external teams, designing standardized analytics frameworks that streamlined workflows and embedded performance‑oriented monitoring, through query optimization, automation scripts, AWS orchestration, and dashboards using analytical and visualization tools like Spotfire, Python and SQL.
Improved client retention rate by 25% in six months by delivering three key parameters like radio frequency, network infrastructure planning, and RDD, aligned with stakeholder requirements, transforming the wireless automation suite into a KPI-driven decision framework through automation, data modeling, and interactive dashboards using Spotfire, SQL, and Python.
Reduced anomaly related escalations by 16% in three months by engineering standardized execution frameworks that codified validation logic and query optimisation with automated anomaly detection and streamlined operational workflows through batch pipelines using Spotfire dashboards, AWS, Python and SQL.
Improved database throughput from 70% to 92% in three months by resolving slow‑running queries and embedding partitioning with caching strategies, through query refactoring, indexing, and automated performance checks using data analysis tools like SQL.
Enhanced decision‑making efficiency from 65% to 90% in six months for clients by conducting end‑to‑end data analysis and building interactive dashboards that mapped business performance holistically, using SQL modelling, Python scripts, and Spotfire visualization.
Achieved 25% increase in decision‑making efficiency in six months for clients by conducting end‑to‑end data analysis and building interactive dashboards that mapped business performance holistically, via modelling, automation scripts, and visualization using tools such as Spotfire, Python and SQL

AWSSQLSpotfirePythonData Engineering