Senior Data Engineer with Cloud ETL Expertise

Anusha Yanna

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

Mobile: 945-***-****

LinkedIn: www.linkedin.com/in/anusha-yanna/

Senior Data Engineer

PROFESSIONAL SUMMARY

Delivered 4+ years of data engineering experience building scalable ETL and ELT pipelines across Azure, AWS, and GCP for analytics, governance, reporting, and integration.

Specialized in Python, SQL, Spark, Databricks, Airflow, and dbt to create trusted data products, reusable models, resilient orchestration, and enterprise-scale cloud transformations consistently organizationwide.

Strengthened data quality, lineage, metadata, observability, and security controls while enabling governed datasets for business intelligence, operational reporting, advanced analytics, and compliance initiatives.

Collaborated with analysts, engineers, and business teams to automate ingestion, optimize performance, and deliver analytics-ready datasets supporting enterprise decision-making across regulated environments consistently.

Facilitated leadership skills to inspire teams, resulting in a 20% increase in project efficiency.

Mentored development teams to enhance technical skills, leading to a 15% reduction in error rates.

Collaborated within a shared services environment to streamline processes, improving cross-departmental communication by 30%.

Mentored and guided teams to foster professional growth, boosting team morale and retention by 25%. TECHNICAL SKILLS

Cloud Platforms - AWS (EC2, Lambda, Glue, S3, Kinesis, IAM, EKS, Redshift), Azure (ADF, Synapse, Azure SQL, Entra ID, Key Vault), GCP (BigQuery, GKE, Cloud Storage)

Infrastructure as Code (IaC) - Terraform, Ansible, ARM Templates, Bicep, CloudFormation, Jenkins, Azure DevOps

Monitoring and Incident Response - New Relic, AWS CloudWatch, Azure Monitor, ServiceNow, RCA, SLA Management

Security and Compliance - IAM, Encryption, NIST 800-53, CIS Benchmarks, PCI-DSS, RBAC, Key Vault, Audit Logging

CI/CD and DevOps - Jenkins, GitHub Actions, Git, GitLab, CodePipeline, CI/CD Pipelines, Shell Scripting, automation pipeline management

Programming & Scripting - Python, SQL, Bash, PowerShell, PL-SQL

Data Engineering - AWS Glue, Azure Data Factory, DBT, Apache Kafka, Spark, Hive, GCP Dataflow, ETL tools, data integration pipelines

Databases - Redshift, Snowflake, Azure SQL, PostgreSQL, MongoDB, MySQL

Dashboards and Visualization - Power BI, Tableau, Looker, AWS QuickSight, Tableau Prep

Containers and Containerization - containers, containerized deployments, OpenShift

Data Analytics Tools - Alteryx, RapidMiner

PROFESSIONAL EXPERIENCE

Activision Blizzard King January 2026 – Present

Automation Data Engineer

Architected Azure Data Factory and Azure Databricks pipelines to ingest gaming data, improving transformation reliability, governed delivery, and downstream analytics consumption across global teams.

Engineered Azure Synapse, ADLS, Python, and SQL workflows to curate enterprise datasets, enabling reusable models, secure integrations, and trusted reporting for business stakeholders organizationwide.

Optimized PySpark and Spark workloads on Azure Databricks, reducing processing bottlenecks, strengthening data quality validation, and accelerating availability of analytics-ready datasets across functions.

Standardized metadata, lineage, and RBAC controls across Azure platforms, aligning governance requirements with secure ingestion patterns and dependable warehouse assets for compliance reporting organizationwide.

Automated Azure DevOps deployments and observability checks for ETL workflows, minimizing manual failures, improving release consistency, and supporting reliable data operations across environments.

Engineered data preparation and workflow automation, enhancing data processing automation efficiency by 60% and reducing operational costs by $200K annually through streamlined processes and advanced analytics.

Orchestrated architecture design and large-scale architecture initiatives, leading enterprise rollouts that improved system scalability by 35% and reduced deployment time by 40% across containerized deployments. Netflix August 2024 – December 2025

Data Engineer

Integrated AWS Glue, Amazon S3, and Amazon Redshift pipelines to consolidate streaming data, improving ingestion resilience, curated datasets, and reporting accuracy across platforms organization-wide.

Configured EMR, Spark, Python, and SQL workflows to process large-scale media data, enabling scalable transformations and reliable delivery for analytics stakeholders enterprise-wide consistently daily.

Validated data quality, lineage, metadata, and PII controls across AWS environments, supporting governed analytics, auditable workflows, and trusted datasets for business stakeholders organization-wide consistently.

Streamlined Airflow orchestration with Lambda, Athena, and APIs, reducing operational overhead, improving job monitoring, and strengthening deployment consistency across cloud workloads consistently.

Analyzed RDS, DynamoDB, and Kinesis integrations on AWS, resolving pipeline issues, optimizing throughput, and supporting timely access to business-critical reporting datasets organization-wide consistently daily.

Architected Operational Insights and enterprise-level governance frameworks, resulting in a 50% increase in compliance adherence and a 20% reduction in audit preparation time through robust data integration pipelines.

Pioneered leadership skills by mentoring development teams, fostering collaboration in shared services environments, and boosting team productivity by 30% through effective mentor guide teams strategies. Walmart October 2023 – August 2024

Data Engineer

Orchestrated BigQuery, Dataflow, and Cloud Storage pipelines for sales, inventory, and supply chain datasets, improving analytic readiness and enabling scalable dashboard consumption enterprise-wide consistently.

Consolidated Dataproc, Spark, and Pub/Sub workloads into governed GCP processing patterns, increasing throughput and supporting near-real-time insight generation for retail operations enterprise-wide consistently today.

Refined semantic models and KPI datasets with SQL, Looker, and Tableau, improving self-service analytics adoption and strengthening executive decision support capabilities enterprise-wide consistently today.

Governed data quality, metadata, and access controls through Data Catalog, IAM, and CI/CD practices, improving trusted reporting and audit readiness across platforms enterprise-wide consistently.

Established GCP IAM, metadata, and lineage controls across analytics platforms, strengthening secure access, data governance, and confidence in shared reporting assets enterprisewide consistently daily.

Modernized PL-SQL and ETL tools, achieving 99.9% reliability in data integration pipelines and optimizing performance by 50% through advanced automation pipeline management techniques.

Revolutionized Alteryx and RapidMiner processes, reducing data processing time by 70% and enhancing code quality, resulting in a 45% increase in data accuracy and consistency. JPMorgan Chase January 2019 – July 2022

Data Analyst Engineer

Designed Azure Data Factory and Databricks pipelines for banking transaction and customer datasets, improving governed availability for finance, risk, and compliance teams enterprise-wide consistently.

Streamlined PySpark and Delta Lake transformations on Azure Synapse, standardizing curated records that strengthened trusted reporting for treasury and regulatory stakeholders enterprise-wide consistently today.

Implemented Snowflake, dbt, and Azure DevOps release workflows, improving deployment repeatability and accelerating secure delivery of enterprise-grade financial data products organization-wide consistently today successfully.

Monitored pipeline quality, lineage, and observability through Airflow and Purview controls, reducing production incidents and increasing confidence in business-critical datasets enterprise-wide consistently today securely.

Enhanced Power BI and Azure SQL reporting layers with curated datasets, accelerating finance analytics, reusable semantic models, and stakeholder decision support enterprise-wide consistently daily.

Engineered Tableau Prep and OpenShift solutions, achieving a 40% improvement in task dependency tuning scheduling scalability and reducing troubleshooting time by 35% in shared services environments.

Quantified performance optimization and containerized deployments, resolving performance issues in scrum teams, and increasing application efficiency by 25%, significantly enhancing task dependency tuning scheduling scalability.

EDUCATION

Master's in Information Systems and Technology - University of North Texas

Bachelor’s in Engineering - IARE

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