Data Engineer Processing

Mukesh Reddy Ainala

Michigan, MI 989-***-**** ***************@*****.***

Professional Summary

Data Engineer with 3+ years of experience in architecting scalable ETL pipelines on AWS and GCP. Proficient in Python, SQL, and data warehousing (Snowflake, Redshift), with hands-on expertise in real-time data streaming and agile methodologies. Improved operational efficiency and performance by collaborating closely with cross-functional teams to streamline data processing and drive robust, cost-effective solutions.

Technical Skills

• Cloud Platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS)

• Data Engineering: BigQuery, Snowflake, Cloud SQL, Redshift, RDS, Dataflow, AWS Glue, GCP Databricks, Data Modeling, Data Warehousing, Distributed Computing

• ETL Tools: Matillion, SSIS, Data Pipelines

• Orchestration & Workflow: Cloud Composer (Airflow), Jenkins, Terraform

• Programming: Python, Shell Scripting, Ruby, Scala, Java

• AI/ML Tools: Vertex AI, Jupiter Notebooks, ML Ops, LLM Ops

• DevOps Tools: GitHub, Maven, Artifactory, Chef, Docker, Kubernetes

• Security & Governance: IAM, VPC Service Controls, DLP API

• Dashboards & Reporting: Looker Studio, Data Studio, SSRS

• Monitoring: Cloud Monitoring, Stackdriver, CloudWatch

• Databases: MySQL, SQL Server, Oracle, DB2, NoSQL

• Operating Systems: UNIX/Linux

• Methodologies: Agile Practices

Professional Experience

First consulting group Jan 2024 - Present

GCP Data Engineer Michigan, MI

• Designed and maintained enterprise-grade ETL pipelines on GCP using BigQuery, Cloud SQL, and Cloud Storage for both batch and streaming data, ensuring efficient data integration and adherence to data structures and modeling best practices.

• Developed ETL/ELT pipelines with Matillion to integrate data from MySQL, SQL Server, Oracle, and DB2, reinforcing robust and scalable data pipeline architectures.

• Enabled near-real-time data processing with Pub/Sub, Cloud Functions, and Dataflow, showcasing proficiency in managing high-vol- ume streaming data within an Agile environment.

• Managed data pipeline orchestration with Apache Airflow via Cloud Composer, streamlining automation and supporting end-to-end ETL workflow maintenance.

• Optimized BigQuery performance through table partitioning, clustering, and materialized views while crafting efficient SQL queries to support cost-effective and high-performance querying.

• Authored reusable Python modules and analytics workflows that supported both data processing and machine learning development, aligning with core scripting requirements.

• Engineered ML Ops and LLM Ops frameworks on GCP Vertex AI for automated model lifecycle management, collaborating with cross-functional teams to advance data analytics initiatives.

• Implemented AI governance and compliance frameworks, ensuring ethical AI practices and adherence to regulatory standards.

• Configured GCP Data Catalog for centralized metadata management and lineage tracking, enhancing documentation and data quality measures.

• Deployed infrastructure with Terraform and established secure hybrid cloud environments using VPN and interconnect to facilitate seamless integration between GCP and on-prem systems.

• Implemented monitoring and alerting with Cloud Monitoring and Stackdriver to promptly troubleshoot and maintain operational performance.

• Cross-trained teams and provided technical support on GCP and ETL best practices, bolstering collaborative efforts and continuous learning in an Agile setting.

Infinite computer solutions, India Oct 2020 - Aug 2022 AWS Data Engineer

• Engineered scalable ETL pipelines using AWS Glue and Python to enable efficient processing and transformation of large, diverse datasets, supporting core extraction, transformation, and loading operations.

• Architected optimized data storage solutions with AWS S3, RDS, and Redshift by applying robust data modeling and SQL best practices, aligning with data warehousing requirements.

• Integrated Snowflake for high-performance querying and cost-effective storage, leveraging relational databases and cloud data warehousing expertise.

• Automated infrastructure deployments with AWS CloudFormation and managed delivery cycles with Jenkins and Maven to ensure reliable system integration and continuous pipeline enhancements.

• Streamlined load balancing and message queuing with AWS ELB and SQS to improve data flow and support external system interfacing for enhanced data integration.

• Monitored system performance with CloudWatch, ensuring that data processing operations consistently met performance benchmarks.

• Collaborated on CI/CD pipeline enhancements with GitHub and Jenkins, accelerating development cycles and integrating agile methodologies into data integration practices.

• Documented processes using JIRA, Confluence, and Remedy, ensuring comprehensive tracking and clear documentation of system architectures and data workflows.

• Developed PowerShell scripts to automate routine tasks, enhancing operational efficiency and reducing manual processing efforts. Education

Central Michigan University Aug 2022 - May 2024

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