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krishna deepika - Data Engineer

[email protected]

Location: Charlotte, North Carolina, USA

Relocation: Yes, I'm open to relocation if required.

Visa: F1-OPT

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Krishna Deepika [email protected] | 980-781-8700 Summary Certified Azure and AWS Data Engineer with 4+ years of experience designing and delivering cloud-native data pipelines and distributed data systems across AWS, Azure, and GCP. Proven expertise in ETL/ELT workflows, real-time streaming with Kafka/MSK and Spark Structured Streaming, and scalable pipeline orchestration with Airflow and Argo. Skilled in Generative AI (Gemini, Agentic) integration, embedding LLM-powered automation into pipelines for anomaly detection, intelligent reporting, and advanced analytics. Strong foundation in data warehousing and modeling (Snowflake, Redshift, BigQuery, Synapse), schema design, lineage tracking, and query optimization for performance at scale. Experienced in containerization (Docker, Kubernetes) and CI/CD (GitHub Actions, Jenkins, Terraform) for secure, automated deployments in production environments. Adept at cross-functional collaboration, translating business requirements into production-grade data products that drive measurable business value. Hands-on background in data governance, metadata management, and BI enablement using PostgreSQL, Cassandra, Power BI, and Tableau. TECHNICAL SKILLS Programming & Scripting: Python, SQL, Java, Scala, R, BashBig Data & Streaming: Apache Spark, Kafka, Flink, Kinesis Cloud & ETL Tools: AWS (S3, Lambda, Glue, Redshift, SageMaker), Azure (ADF, Blob, Synapse), Airflow, dbt Data Warehousing & Modeling: Snowflake, Redshift, BigQuery, Synapse, Star/Snowflake Schema Design, Partitioning, Clustering Data Pipelines & Orchestration: Batch & Real-time Pipelines, Apache Airflow, Argo, dbt DevOps, CI/CD & Infra-as-Code: GitHub Actions, Jenkins, Terraform, Docker, Kubernetes, Git MLOps & AI Infrastructure: SageMaker Pipelines, MLflow, Model Monitoring, AI/LLM Infrastructure, LLMOps, RAG Pipelines Vector & NoSQL Databases: Pinecone, FAISS, Cassandra, PostgreSQLData Visualization & BI: Power BI, Tableau, Grafana Collaboration & Productivity: JIRA, Slack, Confluence, Trello, Notion Business & Communication: Stakeholder Engagement, Requirement Translation, Data Governance, Documentation EDUCATION Master of Science, Data Science and Business Analytics. University of North Carolina at Charlotte. Artificial Intelligence. Winter Program. Asia University, Taiwan. Bachelor of Technology. SRM University, India. CERTIFICATION AWS Certified: Data Engineer Associate. May 2025 Microsoft Certified: Azure Data Engineer Associate | Nov 2024 Coursera: Machine Learning in Production | Feb 2022 Microsoft Verzeo : Machine Learning/AI Intern Coursera: Python (Crash course/ Intermediate/ Advance) PROFESSIONAL EXPERIENCE Acer America July 2024 Present Role: Data Engineer Responsibilities: Designed and deployed cloud-native data pipelines using Apache Spark (RDD/DataFrame) and AWS Glue, reducing daily processing latency by 62% across 5M+ device telemetry records. Built real-time ingestion systems using Amazon MSK (Kafka) and Kinesis Data Streams, enabling sub-minute analytics for performance alerts and device monitoring. Orchestrated 85+ production pipelines using Apache Airflow with SLA monitoring, auto-retry logic, and integrated Slack/JIRA alerts to ensure high reliability. Modeled and maintained analytical datasets in Amazon Redshift, S3, and PostgreSQL, incorporating schema versioning, lineage tracking, and access controls for ML and BI use cases. Developed automated data quality checks with Great Expectations and Python, reducing anomalies by 40% and improving reporting trustworthiness. Collaborated with data scientists to deploy predictive failure detection models using Amazon SageMaker, achieving a 25% improvement in proactive maintenance accuracy. Led phased migration from on-prem ETL to S3, Lambda, and Step Functions, boosting scalability and cutting operational costs by 45%. Implemented Terraform-based IaC and built GitHub Actions CI/CD pipelines for automated testing, deployment, and rollback of production workflows. Acer America Jan 2024 Jun 2024 Roles: Data Engineer Responsibilities: Built and deployed scalable ETL pipelines using Python and Apache Airflow, optimizing data refresh cycles and reducing batch latency by 28% across critical sales and inventory systems. Developed high-performance SQL queries and Redshift materialized views for Power BI dashboards, reducing report load times by 35% and enhancing executive decision-making. Engineered cloud-based data ingestion layers using Amazon S3, AWS Lambda, and Amazon Event Bridge, supporting a 20% increase in data volume with stable throughput. Created robust data validation frameworks using Pandas, custom rule engines, and anomaly detection scripts, leading to a 40% drop in data inconsistencies and higher stakeholder trust. Contributed to the migration of legacy ETL systems to Amazon Redshift and AWS Glue, resulting in a 50% reduction in maintenance overhead and significantly faster query performance for analytics teams. University of North Carolina at Charlotte Aug 2023 Dec 2023 Roles: Graduate Research Assistant Responsibilities: Supported academic teams in building automated ETL pipelines for research and departmental reporting using Python, SQL, and Airflow. Developed Snowflake data models and orchestrated cloud-based ingestion workflows using dbt and AWS S3, simulating production-grade architecture. Built a real-time analytics pipeline using Kafka + Spark Structured Streaming, delivering mock e-commerce insights to PostgreSQL and Power BI dashboards for visualization. Simulated an IoT data monitoring system using Flink, Kinesis, Cassandra, and Grafana, enabling real-time telemetry tracking and performance alerting. Managed infrastructure-as-code for deployment using Terraform, and integrated CI/CD pipelines via GitHub Actions for automated testing and release control. Cooper Standard March 2020 - June 2022 Role: Data Analyst Responsibilities: Performed data analysis and created insightful reports for the client, helping improve operational efficiency by 15% through data-driven decision-making. Utilized SQL and Python to extract, clean, and analyze large datasets (over 50,000 records), ensuring data integrity and accuracy. Developed interactive dashboards using Power BI, enabling real-time tracking of key performance indicators (KPIs) for stakeholders. Conducted exploratory data analysis on customer data, identifying trends and insights that led to a 10% increase in customer satisfaction. Collaborated with cross-functional teams to define and implement data strategies, improving reporting timelines by 20%. Automated manual reporting processes using Python scripts, reducing reporting time by 30%. Identified data anomalies and inconsistencies, investigating and resolving issues to maintain high data quality standards. Contributed to the preparation of monthly and quarterly performance reports, providing insights into trends and future projections. Mphasis July 2019 - Feb 2020 Role: Data Analyst Responsibilities: Assisted in gathering, cleaning, and transforming large datasets for analysis using Excel, SQL, and Python, improving data quality by 30%. Conducted exploratory data analysis (EDA) to uncover insights and trends, providing actionable recommendations that led to a 15% increase in team efficiency. Developed automated reporting dashboards using Power BI, reducing manual report generation time by 40%. Collaborated with cross-functional teams to understand data requirements and translated them into clear analytical solutions, enhancing communication between departments. Utilized SQL to query relational databases and performed complex joins and aggregations to support business decisions. Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree rlang