Senior Data Analyst - Data Quality ETL

DEEPTI SINGH

Senior Data Analyst Data Quality ETL & Data Engineering

Bengaluru, Karnataka 560102 +91-958******* *************@*****.*** linkedin.com/in/deepti-singh-67bba7218 PROFESSIONAL SUMMARY

Senior Data Analyst with 6+ years across data analytics, ETL testing, data quality, and data engineering. Skilled in SQL, Python, PySpark, AWS, Airflow, Trino, data validation, data reconciliation, and ETL pipeline testing. Analyzed 900M+ records, cut data inconsistencies 70%, automated data-quality workflows, and supported 35+ major product releases. Partners with Engineering, BI, and business stakeholders in Agile/Scrum environments. TECHNICAL SKILLS

Data Analysis & BI: KPI Dashboarding, Executive Reporting, Statistical & Trend Analysis, Data Visualization, Apache Superset (BI tooling comparable to Power BI and Tableau)

SQL & Data Modeling: Advanced SQL, Query Optimization, Data Warehousing, BI Testing ETL & Data Engineering: Extract, Transform, Load (ETL) Testing, PySpark, Python, Source-to-Target (S2T) Mapping, Data Reconciliation, Data Transformation, Data Pipeline Management, Trino Data Quality & Testing: Data Validation, Data Integrity Checks, Data Profiling, Data Quality Testing, Data Governance, Regression

& Performance Testing, API Testing & Integration, Root Cause Analysis (RCA) Cloud, Tools & Methods: AWS (S3, EMR), Apache Airflow (DAGs), Jira, Git, Agile/Scrum PROFESSIONAL EXPERIENCE

Data Analyst III — Draup, Bengaluru Jan 2026 – Present

• Designed scalable PySpark pipelines on AWS EMR and data-validation frameworks that maintained zero critical reporting failures across production releases

• Cut manual intervention 70% by automating data cleansing, transformation, and quality controls in PySpark via Airflow DAGs

• Built 8 executive KPI dashboards (15–20 visualizations each) and 50–70 operational issue-tracking dashboards from multi-source datasets

• Analyzed 900M+ structured and semi-structured records stored in AWS S3 to surface trends that informed growth and operational decisions

• Standardized reporting definitions with Engineering, BI, and business teams, improving ETL deployment reliability in Agile

• Reduced Airflow DAG-failure triage time 65% (45–60 min to 15–20 min) through daily alert monitoring that flagged production/pre-production S3 path and sync drift before it reached reporting

• Improved issue diagnosis time ~95% (3–4 hours to ~10 min) by building feature-level Root Cause Analysis (RCA) tooling

• Applied AI/LLM tools to build workflow automation solutions for RCA and self-serve reporting for stakeholders and senior management, delivering deeper data-quality insights without manual report preparation Data Analyst II — Draup, Bengaluru Sep 2022 – Jan 2026

• Directed data validation across enterprise ETL pipelines processing 850M+ records over 30+ releases and 6 vendor migrations, verifying source-to-target-staging mappings and transformation logic

• Built and ran 500+ validation scenarios (functional, regression, performance) in PySpark, SQL, and Python; standardized frameworks that lowered validation cycle time 60% (~20 hours per release)

• Decreased data inconsistencies 70% and undetected production errors 30% through data-quality analysis, root-cause investigation, and Jira/Hypertest issue tracking

• Resolved complex ETL data-quality issues alongside data engineers and mentored 7 junior analysts on data validation and quality best practices

• Validated 5 major legacy database migrations to production, ensuring data integrity by verifying source-to-target mappings and rigorously testing Airflow DAG runs across S3 staging paths

• Applied AWS cloud data fundamentals to optimize S3 storage costs and improve query performance of PySpark and Trino workloads on EMR within the existing infrastructure

• Implemented data cataloging processes for S3 datasets and Airflow-managed pipelines, improving metadata visibility and simplifying information retrieval for internal engineering teams

• Drafted a long-term data strategy to align AWS and Airflow infrastructure upgrades with shifting business requirements Software Development Engineer in Test I — Addverb Technologies, Noida Feb 2020 – Sep 2022

• Led release planning and regression testing across 35 major and 90 minor WMS/WCS releases, defining quality standards and governance that improved release reliability in Agile/Scrum

• Automated performance-testing scenarios for AGV and ASRS workflows with 11 major and 60 minor reusable test scripts, cutting manual validation effort; mentored 4 junior QA engineers

• Designed end-to-end test strategies for intralogistics and warehouse automation systems (WMS, WCS, AGV, ASRS, conveyors), boosting test efficiency ~50% across supply chain fulfillment deployments

• Ran full-cycle system testing of robotics and conveyor systems with 150+ test cases per project and presented findings and recommendations to management

• Authored technical documentation for system architecture and WMS/WCS API integrations to assist with onboarding and knowledge sharing

• Analyzed system performance metrics to pinpoint 10 major infrastructure bottlenecks; recommended actionable improvements projected to enhance system responsiveness by 25% and reduce downtime during peak usage periods

• Collaborated cross-functionally with engineering and product teams to align technical deliverables with project requirements

• Adopted a customer-focused approach to refine product features based on direct feedback and usage patterns EDUCATION

Bachelor of Technology, Computer Science Engineering — Galgotias University, Greater Noida 2016 – 2020

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