Manish Karri

• Over 5 years of experience in Data Engineering with expertise in complex SQL and Python data analysis.
• Strong proficiency in ETL (Extract, Transform, Load) processes, ensuring efficient and accurate data movement and transformation.
• Skilled in Ruby programming, adding versatility to the skill set and enabling automation of data engineering tasks.
• Proven record of designing, building, and optimizing large-scale data pipelines and processing frameworks.
• Expertise in data modeling and warehousing concepts, working with tools such as Redshift, Snowflake, and BigQuery.
• Adept at working with both SQL and NoSQL databases, including MySQL, MongoDB, and Cassandra.
• Proficient in data visualization tools like Tableau and Power BI, including data cleansing, transformation, and data integrity.
• Experienced in statistical methods and machine learning algorithms for data analysis and interpretation.
• Strong communicator, facilitating effective collaboration with cross-functional teams and stakeholders.
• Proficient in Apache Airflow for orchestrating data workflows and pipeline scheduling.
• Active participant in code reviews, fostering a culture of collaboration among teams.
• Knowledgeable in cloud-based data platforms like AWS, Azure, and GCP, with hands-on experience in services like S3, EC2, and EMR.
• Proficient in Unix/Linux operating systems, efficiently navigating, administering, and optimizing data-related tasks.
• Strong command-line expertise, enabling efficient data manipulation and system management.
• Skilled in shell scripting using Bash and Python to automate data processing and ETL tasks.
• In-depth knowledge of Unix-based tools and utilities like sed, awk, grep, and cron for effective data extraction, transformation, and scheduling.
• Implementation of data quality checks to ensure data accuracy, completeness, and anomaly detection.
• Experience in machine learning model deployment using tools like TensorFlow, PyTorch, and MLflow.
• Knowledge of Agile development methodologies, DevOps practices, and CI/CD pipelines for efficient and scalable data engineering solutions.
• Proficient in conducting A/B testing and statistical analysis to assess the effectiveness of marketing campaigns.
• Strong problem-solving skills and a creative approach to developing data solutions.
• Familiarity with data security and privacy measures, ensuring regulatory compliance.
• Ability to work independently and effectively manage multiple projects.
• Skilled in using R and Excel for predictive modeling and forecasting.

Predictive analytics for customer churn: April 2018

- Analyzed customer data for a telecom company to identify customers who are likely to churn.

- Used techniques such as logistic regression, decision trees, or random forests to build a predictive model.

- Evaluated the model's performance using metrics such as accuracy, precision, recall, and F1 score.

- The project required data collection, cleaning, preprocessing, and analysis skills.

- It also required knowledge of machine learning and statistical techniques.

- Programming skills in languages such as Python, R, or Java are needed, as well as proficiency in data analysis and visualization tools.

Sentiment Analysis of Social Media Data Using NLP and Machine Learning: May 2017

- Collected data from social media platforms such as Twitter and analyzed it to determine the sentiment of users towards a particular topic.

- Used techniques such as natural language processing (NLP), machine learning, and text analytics to classify tweets or posts as positive, negative, or neutral.

- Visualized the sentiment distribution using tools such as Matplotlib or Tableau.

- The project required data collection, cleaning, preprocessing, and analysis skills.

- It also required knowledge of NLP, machine learning, and statistical techniques.

- Programming skills in languages such as Python, R, or Java are needed, as well as proficiency in data analysis and visualization tools.