Available to hire
Hi, I’m Vivek Vardhan Rao Sirikonda, a Data Engineer with around 5 years of experience across the full SDLC. I design and implement data pipelines, clean and transform data, and build predictive models using Python, SQL, Spark, and ML frameworks. I have hands-on experience with AWS, Azure, GCP, and various ETL and big data tools, delivering scalable data solutions across Retail, Healthcare, Telecom, Insurance, and Financial Services.\n\nI enjoy collaborating with cross-functional teams to translate business needs into data-driven insights. My work spans data collection, cleaning, analytics, and MLOps, including deploying and monitoring models in production. I’m a proactive learner who thrives in fast-paced environments and loves turning complex data into actionable business outcomes.
Work Experience
Data Engineer at Genesis Healthcare
March 1, 2024 - PresentGathered business requirements with stakeholders, defined data analysis objectives, and built ETL pipelines to collect datasets from databases, data warehouses, APIs, and flat files. Queried data using SQL and Pandas; retrieved data from AWS Redshift and Snowflake; performed data cleaning, manipulation, profiling, and data mining. Developed Azure Databricks notebooks for transformations and cleansing; designed Azure Data Factory pipelines to ingest data from relational and non-relational sources. Implemented statistical techniques and ML algorithms (classification, regression, clustering, time series, DL/NLP) to create scalable business solutions. Facilitated MLOps deployment and monitoring via AWS SageMaker and Databricks; migrated data via S3 and API Gateway. Used Alteryx and Dataiku for data prep and modeling; migrated SSIS packages to ADF. Generated dashboards in Tableau/Power BI and supported supply chain and risk management analytics. Domain: Retail.
Data Engineer at Kanerika Inc, India
December 1, 2021 - July 1, 2023Involved in full SDLC, building data models and ETL pipelines (extract, transform, load) using Google BigQuery and Amazon Redshift. Performed data mapping, integration, and transformation with Apache Spark; prepared data, identified patterns, and mentored a data analytics program. Implemented data quality checks in Databricks and built Azure Data Factory pipelines for source-to-sink data movement. Trained teams on statistical distributions and ML algorithms (logistic regression, decision trees, random forests, neural networks, SVM, clustering) using scikit-learn. Built Airflow-based ETL in GCP, utilized Dataproc, GCS, Cloud Functions, and BigQuery; mentored projects on Vertex AI and AutoML. Authored KPI objects and delivered dashboards in Power BI and Tableau.
Data Engineer at Envoy Global, India
March 1, 2020 - November 1, 2021Created Redshift clusters, configured VPC endpoints, and orchestrated multiple AWS Glue jobs to crawl data from S3 to Redshift. Gathered requirements and prepared target-to-source mappings; participated in data collection, cleaning, mining, visualizations, and model development. Used AWS EMR for large-scale data processing and implemented AWS Step Functions to orchestrate Lambda-based tasks. Migrated architecture to AWS, leveraging Kinesis, Redshift, and Lambda; collaborated with GCP and Snowflake for data storage. Performed data processing with NumPy, Pandas, SciPy, and ML model prediction with scikit-learn in Python; explored Hadoop/Hive/Sqoop/Spark-based workflows. Engaged in MLOps and BI dashboarding with Tableau/Power BI; focused on personalization, behavior analysis, segmentation, fraud detection, and risk management.
Data Engineer at Genesis Healthcare (Client), Zanesville, OH
March 1, 2024 - PresentGathered business requirements, collaborated with stakeholders to define data analysis objectives, and built ETL pipelines to collect datasets from databases, data warehouses, APIs, and flat files. Queried data with SQL and Pandas, performed data cleaning, profiling, and mining in Python (AWS Redshift and Snowflake). Developed Azure Databricks notebooks for transformations and data cleansing, and designed Azure Data Factory pipelines to ingest data from multiple sources. Applied statistical techniques and ML models (classification, regression, clustering, time-series forecasting, deep learning, NLP) to create scalable analytical solutions. Contributed to MLOps with model deployment and monitoring in production environments using SageMaker or Databricks. Integrated data from S3 via API Gateway for batch processing and supported visualization with Tableau/Power BI for monthly/quarterly transaction performance reporting across markets.
Education
Master of Information Technology and Management at Illinois Institute of Technology, Chicago
January 11, 2030 - January 5, 2026Bachelor of Computer Science at Lovely Professional University, Punjab
January 11, 2030 - January 5, 2026Masters in Information Technology and Management at Illinois Institute of Technology, Chicago
January 11, 2030 - January 26, 2026Bachelor of Science in Computer Science at Lovely Professional University, Punjab
January 11, 2030 - January 26, 2026Qualifications
Industry Experience
Retail, Healthcare, Telecommunications, Financial Services
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