S

Srinidhi Gutta

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Data Engineer | Azure Lakehouse Architect · Databricks · Microsoft Fabric · Finance & R&D
Seattle, Washington, United States
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Résumé

Jobs verified_user 0% verified
  • Microsoft
    Data Engineer
    Microsoft
    Aug 2024 - Current (2 years 1 month)
    At Microsoft, Owned core Fusion Finance Lakehouse components serving FP&A, Cost Governance, and executive reporting; responsible for ingestion, medallion transformations, semantic models, and platform reliability. Key contributions included: 1. Owned and stabilized the month‑end data intake by re‑architecting brittle SQL jobs into ADF‑orchestrated Synapse notebooks with telemetry and schema‑drift detection month‑end incidents decreased ~32% and manual fixes nearly eliminated. 2. Architected and shipped medallion pipelines (Bronze - Silver - Gold) for finance datasets, enabling consistent KPI definitions and reducing downstream reconciliation time for FP&A. 3. Cut cloud compute spend ~28% by tuning PySpark jobs, implementing partitioning/c
  • Quadrant Technologies
    Data Engineer
    Quadrant Technologies
    May 2023 - May 2024 (1 year 1 month)
    At Quadrant, I worked for Unilever. Built the R&D Experiment & Formulation Lakehouse to unify lab, experiment, and formulation data and deliver trusted analytics to global R&D teams. Key contributions included: 1. Consolidated 12+ lab and experiment systems into a single Delta Lake lakehouse using ADF and PySpark ingestion, creating one source of truth for formulation analysis. 2. Re‑designed ingestion and transformation pipelines to cut experiment processing time ~40%, so scientists could iterate on formulations within hours instead of days. 3. Implemented validation rules, anomaly detection, and schema‑drift monitoring across experiment and ingredient datasets, improving data quality ~35% and reducing manual cleanup. 4. Built curated fa
  • ADP India
    Data Engineer
    ADP India
    Jan 2021 - Jul 2022 (1 year 7 months)
    At ADP, Modernized payroll ingestion and transformation pipelines to meet enterprise SLAs and compliance needs. Key contributions included: 1. Automated multi‑source payroll ETL in ADF + PySpark with multi‑layer validation and anomaly detection, enabling reliable processing of 50M+ records and reducing manual reconciliation. 2. Rewrote critical payroll transforms into optimized T‑SQL and dimensional models in Synapse, cutting runtime ~15% and speeding report generation. 3. Implemented end‑to‑end validation checkpoints and schema‑drift alerts that improved data accuracy ~35% and prevented compliance issues before they reached finance. 4. Improved pipeline reliability and scheduling so payroll cycles met SLAs consistently, removing last‑min
Education verified_user 0% verified
  • The University of Texas at Arlington
    Master's degree, Computer Science
    The University of Texas at Arlington
    Jan 2022 - Dec 2024 (3 years)
    Focused on Big Data, Cloud Computing, and Advanced Database Systems. Completed projects in distributed data processing with Spark, data warehousing on Azure, and machine learning applications. Strengthened expertise in ETL pipelines, SQL optimization, and data governance, directly aligning with enterprise-scale data engineering roles.