Data Engineer

GSrikar Murthy Mokarala

Senior Data Engineer GCP & AWS Data Pipelines PySpark MLOps Gen AI

Email: *********@*****.***

Phone: +1-469-***-****

PROFESSIONAL SUMMARY

Senior Data & Cloud Engineer with 6+ years of experience delivering scalable data engineering, ML, analytics, and cloud modernization solutions for enterprise clients.

Proven track record working in fast-paced contract environments, quickly onboarding into complex ecosystems, and driving end-to-end delivery across AWS, GCP, and Azure.

Designed and implemented scalable data pipelines on Google Cloud Platform (GCP) using Cloud Dataflow (Apache Beam), Cloud Composer (Airflow), and Pub/Sub to support AI/ML model training and inference.

Built feature engineering pipelines using Big Query, Data form, and Cloud Storage, Airflow enabling consistent and reusable features for ML models.

Strong expertise in building production-grade data pipelines, cloud data warehouses, and analytics-ready datasets that directly support business reporting, insights, and decision-making.

Developed feature engineering pipelines using AWS Glue, Athena, and Redshift, creating reusable feature sets for ML models.

Known for clear communication with stakeholders, hands-on execution, and consistent delivery of measurable outcomes.

CONTRACT HIGHLIGHTS

6+ years supporting enterprise clients in contract and consulting engagements

Rapid onboarding into new client environments, tools, and data platforms

Led cloud migration and modernization projects across AWS and GCP

Delivered analytics-ready datasets supporting BI, reporting, and ad-hoc analysis

Strong collaboration with business analysts, product owners, and data science teams Comfortable owning deliverables end-to-end, from requirements through production support

Implemented model versioning, experiment tracking, and artifact management using Vertex AI Experiments and ML Metadata.

CORE STRENGTHS

Cloud Data Engineering (AWS, GCP, Azure)

Data Warehousing & Analytics (BigQuery, Redshift, Synapse)

Data Pipelines (Batch & Streaming)

Data Analysis & Visualization

Migration & Modernization Projects

Contract-Based Delivery & Client Engagement

TECHNICAL SKILLS

Cloud Platforms: AWS, Google Cloud Platform, Microsoft Azure

Data Engineering: Spark, PySpark, Airflow, Kafka, Data Fusion, Dataproc, EMR

Databases: BigQuery, Redshift, Synapse, SQL Server, Oracle, PostgreSQL

Analytics & BI: SQL, Tableau, Power BI, Looker

Programming: Python, SQL, Scala, Shell Scripting (IDE: PyCharm, CursorAI, VSCode).

Version Control & CI/CD: Git, Azure DevOps, GitHub

PROFESSIONAL EXPERIENCE

Client: CVS Health, Texas Sept 2024 – Present

Senior GCP Data Engineer

Responsibilities:

Partner with business stakeholders and analysts to translate reporting and analytics requirements into scalable GCP-based data solutions.

Designed and implemented Big Query-centric data architectures supporting large-scale analytics and reporting use-cases.

Built scalable ETL/ELT pipelines using Dataproc, Cloud Data Fusion, Dataflow, and Pub/Sub for batch and real-time processing.

Developed Python-based ingestion frameworks and REST API integrations to support near real-time and batch data processing.

Orchestrated complex workflows using Cloud Composer (Airflow), ensuring reliable scheduling, dependency management, and monitoring.

Optimized Big Query schemas, partitioning, and SQL queries to improve analytical performance and reduce operational costs.

Developed a generative AI solution using Gemini Flash 2.5 to process and analyze unstructured call notes, extracting relevant member-level features and insights to support and enhance care management decision-making.

Collaborated with data scientists to produce ML models, by building ML Pipelines and feature engineering workflows.

Productionized machine learning models on Google Cloud Platform using Vertex AI, establishing a centralized model registry for versioning and lifecycle management, and building scalable inference pipelines.

Support data analysts and reporting teams by delivering curated datasets for dashboards and ad-hoc analysis.

Designed and implemented CI/CD pipelines using Git-based version control and GitHub Actions by building YAML-based workflows, enabling automated deployments to Google Cloud Storage on code merges to the main branch.

Environment: GCP, Big Query, Airflow, Dataproc, Vertex AI, MLOps, Gen AI, Cloud Data Fusion, Pub/Sub, Dataflow, Composer, Python, SQL, Docker, Kubernetes

Client: Charter Communications, Denver, CO Jun 2022 – Aug 2024

Data Engineer – AWS

Responsibilities:

Designed and Built data pipelines using PySpark, SparkSQL and AWS Services(EMR, Redshift, Glue, S3, Lambda, Step functions).

Enabled model versioning, experiment tracking, and artifact management using SageMaker Experiments and Model Registry.

Designed and implemented AWS-based data pipelines using S3, EMR, Glue, Redshift, Lambda, and Kinesis for batch and streaming workloads.

Migrated legacy on-prem Hadoop and Teradata workloads to AWS Redshift and EMR, improving scalability and operational efficiency.

Developed PySpark and Spark SQL jobs for data transformation, aggregation, and analytical dataset preparation.

Optimized analytics and ML workloads using Redshift Spectrum and S3-based data lakes following Lake House architecture.

Built ELT workflows supporting downstream analytics, reporting, and data science use cases.

Collaborated with data analysts to deliver clean, well-modeled datasets aligned to business KPIs and reporting needs.

Supported exploratory data analysis using Python, Pandas, and visualization libraries to validate data quality and trends.

Tuned Spark jobs for performance, memory usage, and cost optimization in cloud environments.

Worked closely with DevOps and platform teams to support contract deliverables, releases, production support and implement CI/CD pipelines to automate deployments.

Environment: AWS (S3, EMR, Glue, Redshift, Lambda, Step functions), Jenkins, GCP, PySpark, SQL, Python, Airflow, Git

Capgemini, India Jun 2020 – Dec 2021

Data Engineer

Responsibilities:

Delivered data engineering and analytics solutions for enterprise clients as part of distributed delivery teams.

Designed and developed Spark-based data pipelines for large-scale data processing and analytics.

Supported data analysts by preparing analytical datasets and feature-ready tables for reporting and ML use cases.

Performed data profiling, cleansing, and validation using SQL, Python, and Hive.

Built dashboards and visualizations using Tableau to communicate insights to business users.

Assisted with AWS Redshift-based data warehouse development and data ingestion workflows.

Contributed to NLP and sentiment analysis use cases using Python and Spark ML libraries.

Environment: Spark, Python, SQL, Tableau, AWS, Hadoop, Hive

Igate Global Solutions, Hyderabad, India Jan 2020 – Jun 2020

Data Analyst

Responsibilities:

Worked closely with business stakeholders to understand reporting and analytical requirements.

Extracted, transformed, and analyzed data from Hadoop and relational databases to support operational reporting.

Developed SQL and Hive queries to create summary datasets, metrics, and trend analyses.

Supported data quality checks and validation processes to ensure reliable analytics outputs.

Created Hive-based reports and ad-hoc analysis used by downstream application and reporting teams.

Collaborated with data engineering teams on schema design and enhancements for analytics consumption.

Environment: SQL, Hive, Hadoop, Python, Excel

EDUCATION

Bachelor of Technology - Jawaharlal Nehru Technological University, Hyderabad (Aug 2016 – May 2020)

Master of Science - Western Illinois University, USA (Jan 2022 – May 2023).

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