Junior Data Engineer with 4+ Years Experience
Resume:
Raghunath Reddy Velmineti
*************@*****.***
Junior Data Engineer
PROFESSIONAL SUMMARY
Results-driven Data Engineer with 4+ years of experience designing, building, and maintaining scalable data pipelines and analytics platforms across healthcare, manufacturing, and commercial domains. Strong expertise in SQL and Python for ETL/ELT development, data transformation, and automation. Proven ability to work with cloud platforms, big data technologies, and visualization tools to deliver actionable insights. Experienced in supporting machine learning workflows, performing statistical analysis, and enabling data-driven decision-making through high-quality dashboards and reports. Adept at collaborating with cross-functional teams to translate business requirements into reliable, production-ready data solutions.
TECHNICAL SKILLS
Programming & Analytics:
SQL, Python, Pandas, NumPy, Statistical Analysis, Data Profiling, Feature Engineering
Data Engineering & Warehousing:
ETL / ELT, Data Pipelines, Data Modeling, Dimensional Modeling, Data Warehousing, OLAP
Cloud Platforms:
AWS (S3, EC2, Lambda, Redshift), Azure (Data Factory, Synapse), GCP (BigQuery – Basic)
Big Data & Processing:
Apache Spark, Hadoop, Hive, Distributed Data Processing
Visualization & BI Tools:
Power BI, Tableau, Excel, KPI Dashboards, Interactive Reports
Machine Learning & AI Support:
Data Preparation, Model Training Support, Regression, Classification, Forecasting, Model Evaluation
Databases:
PostgreSQL, MySQL, SQL Server, Redshift, Snowflake (Basic)
Data Quality & Governance:
Data Validation, Monitoring, Lineage, Access Controls, Documentation, Compliance
Tools & DevOps:
Git, GitHub, CI/CD Pipelines, Airflow (Basic), Jira, Agile/Scrum
PROFESSIONAL EXPERIENCE
Data Engineer
Johnson & Johnson (J&J), United States
June 2024 – January 2026
Designed, developed, and maintained scalable data pipelines ingesting clinical, manufacturing, and commercial data from multiple enterprise systems.
Built robust ETL/ELT workflows using SQL and Python to transform raw, structured, and semi-structured data into analytics-ready datasets.
Implemented cloud-based data solutions on AWS and Azure for secure storage, processing, and reporting.
Processed and analyzed large-scale datasets using Apache Spark and Hadoop to support enterprise reporting and advanced analytics.
Developed dimensional and fact-based data models to support data warehousing and OLAP reporting.
Created interactive dashboards and KPI reports using Power BI and Tableau for executive and operational teams.
Supported machine learning initiatives by performing data cleaning, feature engineering, and statistical analysis for predictive models.
Assisted data scientists with model training, validation, and performance monitoring.
Implemented automated data quality checks, anomaly detection, and monitoring processes to ensure data accuracy and reliability.
Optimized queries and pipelines through indexing, partitioning, and performance tuning, improving reporting efficiency by up to 30%.
Ensured compliance with regulatory and governance standards through access controls, audit trails, and documentation.
Collaborated with business stakeholders, analysts, and engineering teams to gather requirements and deliver scalable data solutions.
Participated in code reviews, CI/CD deployments, and Agile ceremonies to maintain engineering best practices.
Contributed to modernization of legacy systems and migration to cloud-based analytics platforms.
Data Engineer
Nevonex, India
June 2021 – January 2023
Designed and supported enterprise-grade data pipelines for operational, clinical, and business analytics.
Developed SQL and Python-based ETL processes to integrate data from ERP, CRM, and external data sources.
Built and maintained data warehouse structures for reporting and business intelligence.
Utilized Spark and Hadoop for processing high-volume datasets and improving batch processing performance.
Created Tableau and Power BI dashboards to visualize trends, KPIs, and performance metrics.
Conducted exploratory data analysis and statistical assessments to identify patterns and anomalies.
Supported machine learning workflows through data preprocessing, labeling, and validation activities.
Implemented data governance standards, validation rules, and monitoring frameworks.
Improved reporting turnaround time by optimizing data models and queries.
Worked closely with QA, analysts, and business teams to deliver high-quality data products.
Maintained version control using Git and supported CI/CD pipelines for deployment.
Assisted in digital transformation projects by migrating legacy workflows to modern data platforms.
EDUCATION
Master of Science in Data Science
Saint Peter’s University, Jersey City, NJ, USA — 2024
Bachelor of Technology in Computer Science Engineering
Mahatma Gandhi Engineering College, Telangana, India — 2021
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