GCP Data Engineer and Analytics Specialist

Sushanth Kumar

Email: ****************@*****.*** Mobile: +1-314-***-****

PROFESSIONAL SUMMARY

6+ years of experience as a GCP Data Engineer specializing in GCP data engineering. Brings production experience across large-scale enterprise data environments and cloud platforms.

Builds and enhances cloud data architecture using BigQuery, Dataproc, and Cloud Composer for scalable ingestion, processing, and orchestration across enterprise environments.

Applies Python to data engineering workflows and large-scale processing, supporting reliable transformation, integration, and analysis across complex cloud-based datasets at scale.

Performs exploratory data analysis using structured analytical methods to identify trends, validate assumptions, and translate complex datasets into actionable business insights.

Designs and optimizes Tableau dashboards, presenting data clearly through purposeful visualizations that support stakeholder interpretation, reporting, and operational business decision-making.

Connects data engineering foundations with AI/ML concepts, preparing analysis-ready data structures that enable downstream modeling and advanced analytical applications.

Collaborates with partner teams to strengthen target data architecture, align technical requirements, and deliver scalable cloud solutions for analytics stakeholders.

SKILLS

GCP Data Engineering & Cloud Platforms: GCP Data Engineer, Google Cloud Platform (GCP), BigQuery, Dataproc, Cloud Composer

Data Architecture & Engineering: Data Engineering, Data Architecture, Data architecture collaboration and enhancement, Large-Scale Data Environments

Data Analytics & Exploratory Analysis: Data Analytics, Exploratory Data Analysis (EDA), Understanding of data analytics concepts and methodologies, Understanding of AI/ML concepts and applications

Business Intelligence & Visualization: Tableau, Tableau Dashboards, Designing, developing, and optimizing Tableau dashboards

Programming & Data Development: Python, Strong Python knowledge

Data Engineering Practices: Data Architecture Collaboration, Data Architecture Enhancement, Data Environment Optimization, Data Analytics Methodologies

Cross-Functional Collaboration: Partner Team Collaboration, Data Architecture Collaboration, Business Insights, Trend Identification

WORK EXPERIENCE

McKesson - Irving, TX

Senior Data Engineer - Jan 2024 to Present

Architected Data Architecture patterns with Google Cloud Platform for large-scale data environments, standardizing approximately 10 data domains to improve downstream analytics readiness.

Engineered BigQuery data models and SQL transformation workflows across an estimated 5 TB of operational data, reducing recurring analytics preparation time by approximately 30%.

Orchestrated Dataproc processing jobs with Python for approximately 20 daily batch workflows, improving distributed data processing consistency and supporting scalable Data Engineering operations.

Configured Cloud Composer pipelines to automate approximately 15 recurring ingestion and transformation workflows, reducing manual monitoring effort by an estimated 25%.

Performed Exploratory Data Analysis (EDA) with Python and BigQuery across millions of records, identifying data-quality trends and accelerating business insight generation by approximately 20%.

Designed and optimized Tableau Dashboards connected to BigQuery for approximately 30 recurring analytical views, improving stakeholder access to Data Analytics and reducing report turnaround time by an estimated 35%.

Collaborated with partner teams on Google Cloud Platform architecture enhancements across approximately 8 shared data interfaces, reducing integration exceptions by an estimated 15%.

Applied AI/ML concepts with Python to prepare and profile large-scale data environments for approximately 5 analytical use cases, improving feature-readiness and reducing exploratory preparation time by an estimated 20%.

Implemented BigQuery validation checks across approximately 25 critical data fields and daily processing cycles, reducing recurring data-quality defects by an estimated 18%.

Documented Google Cloud Platform data workflows, Tableau Dashboards, and Cloud Composer dependencies for a team of approximately 6 engineers, shortening onboarding and operational handoff time by an estimated 20%.

Technologies Used: SQL, Python, Google Cloud Platform (GCP), BigQuery, Dataproc, Cloud Composer, Data Architecture, Data Analytics, Exploratory Data Analysis (EDA), Tableau, AI/ML

Goldman Sachs - Chicago, IL

Cloud Data Engineer - Jun 2021 to Jun 2023

Built Google Cloud Platform data pipelines with Python and SQL for approximately 12 recurring ingestion workflows, improving cloud-based data availability and reducing manual processing by an estimated 25%.

Developed BigQuery datasets and transformation logic for an estimated 3 TB of structured data, improving query usability for approximately 10 downstream analytical processes.

Orchestrated Cloud Composer workflows across approximately 15 scheduled jobs, reducing missed processing dependencies by an estimated 20% and improving batch reliability.

Implemented Dataproc processing routines with Python for approximately 8 distributed workloads, shortening large-scale transformation runtimes by an estimated 30%.

Collaborated on Data Architecture enhancements across approximately 6 source-system integrations, improving schema consistency and reducing downstream reconciliation effort by an estimated 15%.

Applied Data Analytics methods and Exploratory Data Analysis (EDA) with Python to approximately 2 million records, surfacing trends that improved data-quality investigation turnaround by an estimated 20%.

Supported Tableau dashboard data preparation through BigQuery and SQL for approximately 20 recurring reports, reducing dashboard refresh preparation time by an estimated 25%.

Applied AI/ML concepts to approximately 4 analytical data use cases with Python, improving dataset profiling and reducing repetitive feature-preparation work by an estimated 15%.

Added SQL and BigQuery validation rules across approximately 18 critical fields, reducing recurring transformation errors by an estimated 20%.

Documented Google Cloud Platform, Dataproc, and Cloud Composer operating procedures for a team of approximately 5 engineers, reducing workflow support and knowledge-transfer time by an estimated 20%.

Technologies Used: SQL, Python, Google Cloud Platform (GCP), BigQuery, Dataproc, Cloud Composer, Data Architecture, Data Analytics, Exploratory Data Analysis (EDA)

Kroger - Cincinnati, OH

Data Engineer - Dec 2019 to May 2021

Developed SQL-based ETL workflows for approximately 10 recurring data feeds, improving dataset availability for reporting and reducing manual preparation effort by an estimated 20%.

Built Python data-processing scripts for approximately 8 scheduled workloads, reducing repetitive transformation tasks by an estimated 25% and improving processing consistency.

Created BigQuery analytical tables across an estimated 1 TB of structured data, enabling faster querying for approximately 6 recurring Data Analytics processes.

Implemented Google Cloud Platform storage and processing patterns for approximately 7 source integrations, improving centralized data access and reducing duplicate handling by an estimated 15%.

Performed Exploratory Data Analysis (EDA) with Python across approximately 1 million records, identifying completeness and consistency trends for downstream reporting teams.

Applied Data Architecture principles to approximately 5 source-to-target data flows, improving schema alignment and reducing downstream mapping rework by an estimated 15%.

Prepared Tableau data extracts with SQL and BigQuery for approximately 12 recurring views, shortening reporting refresh preparation time by an estimated 20%.

Added SQL validation checks across approximately 15 recurring data fields, reducing repeat data-quality exceptions by an estimated 18%.

Applied foundational AI/ML concepts with Python to approximately 3 exploratory analytical use cases, improving dataset profiling and reducing initial analysis effort by an estimated 15%.

Documented SQL, Python, and Google Cloud Platform data workflows for approximately 4 recurring processes, reducing operational handoff time by an estimated 20%.

Technologies Used: SQL, Python, BigQuery, Google Cloud Platform (GCP), Data Architecture, Data Analytics, Exploratory Data Analysis (EDA)

CERTIFICATIONS

Databricks Certified Data Engineer Professional

Google Cloud Professional Data Engineer

AWS Certified Data Engineer – Associate

Azure Solutions Architect Expert

EDUCATION

Masters Saint Louis University GPA: 4CGPA

Bachelors Annamacharya Institute of Technology and sciences GPA: 74%

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