Rameshwari Jadhav

Data Engineer

Los Angeles, California, United States4 yrs 1 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Proficient in Python, SQL, and data engineering.
Experience with AI and machine learning technologies.
Strong work ethic and exceptional teamwork abilities.

Stackforce AI infers this person is a Data Engineer with expertise in cloud-based data solutions and AI technologies.

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Skills

Core Skills

Data EngineeringCloud InfrastructureArtificial IntelligenceMachine Learning

Other Skills

ETL pipelinesDataflowCloud ComposerBigQuerySQLPythonAITensorFlowPyTorchGCPAzureApache AirflowData migrationData transformationAWS

About

I am seeking a position that will allow me to leverage my skills as an administrator while further developing my technical expertise. I am passionate about programming in Python, SQL, and Java, and have recently started expanding my knowledge in C++ and Data Structures & Algorithms (DSA). As a motivated fresher, I possess a strong work ethic, exceptional teamwork abilities, and the capability to manage multiple tasks simultaneously. In addition to my core programming skills, I am actively enhancing my capabilities in the rapidly evolving fields of Artificial Intelligence (AI) and Machine Learning (ML). I have experience working with Large Language Models (LLMs) and Generative AI (GenAI) technologies, and I am familiar with platforms such as Vertex AI for building and deploying AI solutions. I am eager to apply my skills in data engineering, model development, and cloud platforms (AWS, Azure, etc.) to contribute to impactful and innovative projects.

Experience

4 yrs 1 mo

Total Experience

2 yrs

Average Tenure

2 yrs

Current Experience

Onix

Data Engineer

May 2024 – Present · 2 yrs · Tampa, Florida, United States · Remote

Building and validating ETL pipelines using Dataflow, Cloud Composer, and BigQuery
Designing data quality checks, row-count validation, and schema mapping logic
Automating metadata extraction from legacy scripts using custom Python parsers
Wrote optimized SQL transformations for sales, inventory, and product catalogs
Developed audit logging & row-level lineage for pipeline observability
Built an export-import framework using GCS as an intermediary layer
Worked with Looker and Data Studio for dashboard transition from legacy BI tools
Creating delta-load orchestration using Airflow and GCS change tracking

ETL pipelinesDataflowCloud ComposerBigQuerySQLPython+2

California state university, northridge

Graduate Student Assistant (Software Engineer)

Aug 2023 – May 2024 · 9 mos · Los Angeles, California, United States · On-site

As a Teaching Assistant for graduate-level courses in Artificial Intelligence, Machine Learning, and Data Science, I supported academic and research activities focused on advanced technologies shaping today’s digital world.
1. AI & Machine Learning Support
Mentored students on supervised and unsupervised learning algorithms, including decision trees, SVM, k-means, and ensemble methods.
Provided 1-on-1 guidance on model development, hyperparameter tuning, and evaluation techniques using tools such as Scikit-learn, TensorFlow, and PyTorch.
2. Deep Learning & NLP
Led lab sessions covering neural network architectures including CNNs, RNNs, and Transformers, with real-world applications in image classification and language modeling.
Guided projects involving sentiment analysis, topic modeling, and text classification using NLP libraries like NLTK, spaCy, and Hugging Face Transformers.
3. Cloud Computing & Big Data Integration
Helped students deploy ML models and data pipelines using Google Cloud Platform (Vertex AI, BigQuery) and Azure (Data Factory, Databricks).
Assisted with labs focused on scalable ML, AutoML, and serverless architecture using tools like Apache Beam, Dataflow, and Cloud Composer.
4. Capstone & Research Projects
Actively reviewed and advised final-year AI projects, ensuring proper application of ethical AI principles, reproducibility, and compliance with data privacy standards (GDPR/CCPA).
4. Skills Developed & Shared
Python, SQL, TensorFlow, PyTorch, Hugging Face, GCP, Azure, Apache Airflow, BigQuery, Docker, Git, NLP, Data Visualization, and responsible AI practices.

AIMachine LearningPythonSQLTensorFlowPyTorch+5

Datametica

2 roles

Data Engineer II

Promoted

Jul 2022 – Aug 2023 · 1 yr 1 mo · Pune, Maharashtra, India

At Datametica, I played a pivotal role in architecting and implementing large-scale data migration and transformation solutions across cloud platforms including Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. I contributed to multiple end-to-end data engineering projects in highly regulated environments, ensuring scalability, efficiency, and compliance.
1. Google Cloud Platform (GCP)
Led the migration of enterprise data warehouses from Teradata and Hive to BigQuery, ensuring schema integrity and performance optimization.
Designed and deployed Apache Airflow DAGs using Cloud Composer to orchestrate ETL workflows.
Utilized Dataflow (Apache Beam) for real-time and batch data processing pipelines.
Built monitoring dashboards to track data quality and pipeline performance.
2. Amazon Web Services (AWS)
Implemented ELT pipelines using AWS Glue, Lambda, and Athena to enable scalable data ingestion and transformation from S3-based data lakes.
Developed serverless jobs for data cleansing, enrichment, and aggregation using Step Functions and Glue Jobs.
Integrated Redshift with downstream analytics tools like Tableau and Power BI for BI reporting.
3. Microsoft Azure
Built enterprise-grade pipelines using Azure Data Factory (ADF) to extract and transform data across SQL and NoSQL sources.
Leveraged Databricks on Azure for distributed processing and machine learning model integration.
Implemented data validation and transformation logic using PySpark and integrated with Azure SQL Database and Blob Storage for persistence.
🔹 Core Responsibilities
Engineered robust data models for reporting, forecasting, and ML pipelines using star/snowflake schema and partitioning strategies.
Developed and optimized complex SQL queries across distributed datasets for performance improvement (~40% reduction in processing time).
Ensured data governance, version control, and end-to-end documentation to meet compliance and audit requirements.