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Sai Jaswanth Kunku - Senior Data Engineer

[email protected]

Location: Pleasanton, California, USA

Relocation: Yes

Visa: H1B

Resume file: Jaswanth_Kumar_Resume_1783441556207.pdf Please check the file(s) for viruses. Files are checked manually and then made available for download.

SUMMARY ----------------------------------------------------------- Senior ML/Data Engineer with 9+ years of experience building scalable batch and streaming data pipelines across retail, finance and healthcare. Skilled in Spark, Kafka, Flink, Snowflake, Airflow, and modern lakehouse/warehouse architectures with strong SQL, data modeling, and optimization expertise. Designed end-to-end ingestion, transformation, governance, and analytics ecosystems using big-data frameworks, MDM/ETL tools, and cloud-native services. Delivered reliable, compliant, and high-performance data platforms supporting real-time processing, BI reporting, and enterprise-scale analytics. TECHNICAL SKILLS ----------------------------------------------------------- Programming & Scripting Languages: Python, SQL, KQL Databases: DB2, Oracle, MS SQL Server, MongoDB, Cassandra Data Warehousing: Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics Big Data: Spark, Kafka, Flink, Airflow, Databricks ETL/ELT and Orchestration: Informatica, IBM DataStage, DBT, Fivetran, Azure Data Factory Data Visualization & Monitoring: Tableau, Power BI, Alteryx, Splunk, Elasticsearch Cloud Platforms: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) CI/CD & IaC: Git, GitHub Actions, Docker, Kubernetes, Terraform, Databricks Asset Bundles GenAI & ML: LLMs, Prompt engineering, RAG, Text embeddings, Vector databases SDLC & Testing: Agile, Waterfall, A/B testing, unit testing IDEs & Productivity Tools: Visual Studio Code, Github Copilot, MS Office, Lucid Chart, JIRA, Confluence CERTIFICATIONS ----------------------------------------------------------- Databricks Certified Data Engineer Professional Microsoft Fabric Data Engineer (DP-700) IBM Big Data Engineer PROFESSIONAL EXPERIENCE ----------------------------------------------------------- Albertsons Companies Pleasanton, California Data Engineer Mar 2024 Present Responsibilities: Designed and maintained scalable Databricks ETL and ELT pipelines supporting 10+ critical ML models in business growth area across retail, supply chain, and merchandising domains. Translated Snowflake queries/stored procedures from Snowflake to GCP BigQuery for 100+ tables and views without affecting business logic. Resolved data type mismatches, and optimized joins, partitioning, and clustering, reducing query runtimes by 20 to 40 percent. Tuned PySpark and SQL jobs using shuffle optimization, caching, and broadcast joins, improving pipeline throughput, and stabilizing large-scale batch executions. Optimized BigQuery storage usage by auditing and decommissioning more than 2700 obsolete tables, reclaiming 1.1 Petabytes, and reducing monthly cloud costs by 12000$. Developed Python automation scripts to extract and track model-related tables and views, streamlining data migration tracking and reducing manual effort during model cutovers. Upgraded Databricks Workflows and Job API from version 2.0 to 2.1, enabling parallel task execution and reducing workflow runtime by 30 to 50%. Led Azure to Google Cloud Databricks migrations for ML models, including notebook refactoring, data validation, and production deployment, achieving zero post deployment rollbacks. Built and maintained CI/CD pipelines using GitHub Actions and Databricks asset bundles, automating deployments and reducing manual release efforts. Troubleshot and resolved production issues such as timeouts, schema drift, and intermittent failures, reducing recurring job failures by more than 60 percent. Authored runbooks, validation playbooks, and migration documentation improving on call readiness and accelerating root cause analysis. Built interactive monitoring dashboards in Grafana by integrating KQL-based queries, enabling real-time visibility into pipeline health, job runtimes, and system metrics Developed a text to SQL proof of concept using BigQuery metadata enrichment and large language models, improving query generation quality for business users. Contributed to Databricks to Dataproc migration by adapting PySpark jobs and building Cloud Composer, Vertex AI workflows for orchestration and scheduling. Implemented real-time model serving POC using Mosaic AI Model Serving and Google Cloud Run to benchmark latency, autoscaling, and reduce infrastructure costs and achieved <15 RPS. Environment: Python, Snowflake, BigQuery, CloudRun, Vertex AI, Databricks, Delta Lake, Docker, AKS, Azure SQL Grafana, Cloud Composer Amazon Hilliard, OH Data Engineer Jun 2022 Feb 2024 Responsibilities: Orchestrated end-to-end data pipelines by systematically collecting, loading, and transforming sales, product, customers and other supply chain data from enterprise databases into Delta Lakes for BI and Machine Learning. Utilized AWS DMS to implement capture data capture in Aurora DB tables and stream the new data into Kinesis Data Streams. Created optimized buckets with dynamic partitioning, and compression mechanisms and developed Lambda step functions to seamlessly ingest data from Kinesis Firehose into the S3 raw bucket zone. Created Glue ETL jobs and Glue Crawlers in Glue Studio to perform batch processing and ingest the transformed data into the S3 intermediate zone for ad-hoc analysis using AWS Quick Sight. Leveraged Amazon EMR to perform distributed data transformations and aggregations using Hive and Spark, uncovering valuable insights from the stored data. Created Spark SQL scripts in Apache Spark for robust data cleansing, transformation, and filtering operations on Hive tables to export data in Parquet format with snappy compression to S3 for efficient read/write processing. Developed Hive User-Defined Functions (UDFs) written HQL queries to create external tables on S3 with partitions and bucketing techniques for ad-hoc analysis. Conducted meetings with business stakeholders, clients, and director-level members for requirement gatherings and presented various solutions using best practices. Performed data transformations using DBT to develop data marts for the Machine learning team to create models and improve product recommendations. Instantiated, created, and maintained containerization mechanism with continuous integration and deployment of DevOps pipelines by applying automation applications using GIT, Terraform. Monitored data pipelines and resolved issues evolved with data throughput using CloudWatch logs and Cloud Trail logs. Optimized data pipelines and ensured data quality and integrity throughout the migration process, leveraging tools like AWS EMR and Spark SQL for efficient data movement and computation. Environment: Python, Amazon EMR, Amazon DMS, DynamoDB, CI/CD, Linux, Amazon Kinesis, AWS QuickSight, AWS Glue, Amazon Redshift, Spark SQL, Git Citizen s Bank Fairfax, VA Data Engineer Dec 2021 May 2022 Responsibilities: Developed scalable ELT pipelines using Medallion Architecture (Bronze, Silver, Gold) in Microsoft Fabric, migrating accounts, transactions, and mortgage data from enterprise warehouses into OneLake. Designed logical and physical data models using Erwin, enabling seamless migration from DB2 to Fabric Lakehouse and Warehouse, ensuring data consistency and integrity. Built data ingestion pipelines using Data Factory in Fabric, creating parameterized pipelines and linked services to dynamically load on-prem DB2 data into OneLake. Implemented incremental and bulk data loading strategies using COPY INTO and Fabric-native ingestion patterns and built curated gold layer data marts for analytics and reporting teams. Leveraged Fabric Real-Time Analytics (KQL) to monitor pipeline performance, analyze EventHub streams, and proactively detect data inconsistencies and failures. Developed event-driven ingestion pipelines using Event house and Fabric pipelines to process streaming data into Delta tables within the Lakehouse. Built and optimized PySpark notebooks in Fabric for large-scale transformations, data quality validation, type casting, and enrichment to create optimized silver and gold datasets. Used Delta Lake tables in Fabric Lakehouse for efficient storage, ACID compliance, and faster downstream querying. Implemented Auto Loader like incremental ingestion patterns for processing new files in OneLake with minimal latency. Optimized performance by leveraging partitioning strategies, file sizing, and query optimization techniques within Fabric Warehouse and Lakehouse. Collaborated with cross-functional teams to build data APIs and integration layers, enabling seamless data access across internal systems. Deployed and promoted pipelines across environments using CI/CD and Azure DevOps ensuring consistent and reliable releases. Environment: Python, CI/CD, Snowflake, Cosmos DB, ADF, Power BI, Microsoft Fabric, Apache Spark, KQL Tata Consultancy Services Hyderabad, India Big Data Engineer Apr 2019 Aug 2021 Responsibilities: Offered valuable analytical support for the Claims, Ancillary, and Medical Management departments, contributing to data-driven decision-making and process improvements. Loaded data from microservices to Google Cloud Storage using IBM DataStage and performed structural modifications using Spark in Google Data Proc. Implemented change data capture pipelines (CDC) using Google Pub/Sub with Debezium Connector to process data from MSSQL using Google Cloud functions. Configured Pub/Sub topics with Spark Streaming API in Google Compute Engine to fetch near real-time data from multiple sources, such as weblogs into Delta Lake tables for timely analysis and actionable insights. Ingested billions of claim records into the spark cluster and applied transformations and aggregations, resulting in a 30% reduction in data processing time. Designed and implemented Snowflake database schemas architecture, tables, and views to accommodate structured and semi-structured data. Designed and implemented custom visualizations and dashboards using Looker advanced visualization capabilities, providing stakeholders with intuitive and actionable insights. Developed and implemented risk adjustment models for Medicare data, ensuring accurate and compliant reporting. Collaborated closely with Quality Control Teams to develop comprehensive Test Plans and Test Cases, ensuring rigorous testing of systems and validating data accuracy. Used Matplotlib and Seaborn in Python to visualize the data and perform feature engineering to detect outliers and perform normalization. Written Spark applications in Scala to interact with the MSSQL database using Spark SQL Context to access Hive tables. Developed supervised ML models with hyperparameters using Spark MLlib to identify different kinds of fraud in Medicare claims. Developed dashboards and visualizations to help business users analyze data and provide insight to business using SQL Server Reporting Services (SSRS) and Power BI. Environment: Python, Scala, BigQuery, Spark, Dataproc, Power BI, IBM DataStage, SQL Server, Google Dataflow Brainy n Bright Inc. Hyderabad, India Data Engineer Nov 2017 - Mar 2019 Responsibilities: Developed SSIS packages and maintained SQL server agents to perform initial loads and full loads into cloud storage. Written Map reduce code to transform unstructured data into Structured Data into Hive Tables Using Java for querying. Loaded the data from CSV, JSON, XML data sources into HDFS using Sqoop and integrated them into Hive tables. Used Pandas, NumPy, Seaborn, SciPy, Matplotlib, Scikit-learn, and NLTK in Python for implementing various machine learning algorithms. Used Spark-SQL to Load JSON data and create Schema RDD, loaded it into Hive Tables, and handled structured data using Spark SQL. Designed and implemented data pipelines using ThoughtSpot capabilities to process and transform raw data into actionable insights, optimizing for speed and accuracy. Executed scheduled tasks for weekly and monthly data updates while adeptly managing and manipulating the data for efficient database management. Designed dashboards utilizing SSAS and SSRS to construct matrixes as well as tabular reports using reporting services. Environment: Python, CI/CD, Oracle, Hadoop, MongoDB, SSIS, SSAS, SSRS, Spark, MySQL, MongoDB LogicMatter Inc. Hyderabad, India ETL Developer Sept 2016 - Nov 2017 Responsibilities: Extracted, transformed, and loaded Salesforce data (Opportunities, Accounts, Leads, Contacts, Tasks, Events, etc. using Informatica PowerCenter, enabling seamless migration between Salesforce, Teradata, Oracle, and SQL systems. Built ETL and Data Quality mappings, performed deep data profiling, and automated validation/deduplication to improve SFDC data accuracy and reliability. Designed logical and physical data models using ER diagrams, class diagrams, and Erwin Data Modeler to map source target schemas and support smooth migration and integration projects. Developed complex SQL (materialized views, CTEs, nested queries, case logic) and created Power BI reports with KPIs and advanced DAX for ad-hoc analytics. Prepared business requirement documentation and delivered end-user training to ensure successful adoption of new data workflows and reporting solutions. Environment: MS Excel, AWS, Oracle, DB2 , Power BI, ERWIN Data Modeler, Informatica, UNIX, Salesforce EDUCATION ----------------------------------------------------- George Mason University, Masters in Data Analytics Engineering Fairfax, VA GITAM University, Bachelor of Technology in Computer Science Visakhapatnam, India Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree database active directory information technology microsoft mississippi Ohio Virginia