Manikanta Jonnadula
Data Engineer • j*******************@gmail.com • 824****428 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Engineer with 4 years of experience designing and operating ETL/ELT pipelines, data warehouses, and multi-region ingestion frameworks using Python, PySpark, Redshift, Databricks, AWS, and Azure to support analytics and reporting.
Technical Skills
Programming Languages: Python
Databases: SQL,MySQL
Cloud and DevOps: AWS,Azure,AWS S3,AWS Glue,AWS Lambda,AWS EC2,Azure Data Lake,Azure Data Factory,Apache Airflow,GitHub Actions
Data and Analytics: Databricks,ETL,ELT,Data Modeling,Dimensional Modeling,DBT,Amazon Redshift,RDBMS
Tools and Methodologies: Git,Jira,PyCharm,Visual Studio,Confluence
Skills: PySpark
Monitoring & Observability: Splunk,Datadog
Work Experience
MSD (Merck & Co)
Data Engineer
June 2025 – Present
Built and operated vaccination data pipelines for a global healthcare/pharmaceutical company, supporting multi-country reporting and analytics.
Tech Stack: Databricks, PySpark, Amazon Redshift, AWS S3, AWS Glue, GitHub Actions, Git, Confluence, Jira, SQL
- Designed and implemented Bronze/Silver/Gold ETL architecture in Databricks using PySpark to standardize vaccination data ingested from multiple sources for downstream analytics and BI.
- Developed and maintained ELT workflows using PySpark, AWS S3, AWS Glue and Amazon Redshift to process transactional and analytical vaccination datasets across regions.
- Modeled and implemented Fact and Dimension tables in Redshift to support clinical and regional reporting; collaborated with analysts to validate business rules for Germany, France, and Canada.
- Automated CI/CD deployments with GitHub Actions and Git across Dev, UAT, Stage and Production; maintained STTM mapping and Confluence documentation for data lineage and governance.
- Led UAT-level data validation and SQL-based testing to ensure business rule compliance and data accuracy prior to releases; performed query optimization and performance tuning on Redshift loads.
- Managed incidents and change requests through Jira; implemented enhancements and fixes based on stakeholder feedback to improve pipeline reliability and data quality.
PepsiCo
Data Engineer
Dec 2024 – May 2025
Supported retail and supply-chain data ingestion and ETL pipelines for beverage and retail analytics using a hybrid Azure/AWS stack.
Tech Stack: PySpark, Azure Data Factory, Azure Data Lake, AWS S3, AWS Glue, AWS Lambda, SQL, Git, Jira
- Developed and maintained PySpark-based ETL pipelines and Azure Data Factory workflows to ingest retail transaction and supply-chain data into Azure Data Lake.
- Built automated ingestion frameworks writing raw and transformed data to an injection layer on AWS S3 and Azure Data Lake to enable downstream processing and reporting.
- Implemented Lambda and Glue jobs for serverless transformations and metadata handling as part of a hybrid cloud ingestion pattern.
- Optimized SQL queries and PySpark jobs to improve large-scale data processing performance and reduce job runtimes for daily reporting windows.
- Collaborated with business stakeholders to translate reporting requirements into schema designs and transformations, ensuring accurate KPIs and metrics.
- Monitored pipeline health and handled production incidents using logging and ticketing; documented runbooks and change requests to streamline operations.
GSPANN Technologies
Data Engineer
2021 – 2024
Delivered data engineering solutions for travel and retail clients, building scalable ETL pipelines, monitoring dashboards and automation for analytics and finance reporting.
Tech Stack: PySpark, AWS S3, AWS Glue, AWS Lambda, Apache Airflow, Splunk, Datadog, nVision, SQL, Git
- Built and optimized PySpark ETL pipelines on AWS to process large hotel and car booking datasets for a travel client, improving ingestion throughput and downstream availability.
- Designed and implemented scalable retail transaction ingestion frameworks using PySpark, AWS S3, Lambda, Glue and Airflow to support high-volume data capture and processing.
- Automated data extraction, transformation and loading processes and developed custom Python scripts for data cleaning and enrichment before loading into warehouses.
- Implemented monitoring and alerting dashboards using Splunk and Datadog to track job health and system performance; reduced mean time to detect by improving observability.
- Conducted root cause analysis for production failures, implemented error-handling and retry mechanisms in Airflow and Lambda jobs, and authored runbooks for operations teams.
- Integrated financial reporting pipelines with nVision for automated reconciliation and reporting; collaborated with cross-functional teams to deliver business insights and visibility.
Education
Aditya College of Engineering and Technology, Surampalem
Master of Computer Applications (MCA)
Certifications
Python — Simplilearn
SQL — Great Learning
Cyber Security — Ministry of Electronics & Information Technology, Government of India
Azure Fundamentals — Microsoft
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