Data Engineer Intern Resume Samples

Description :

1. Assembled large, complex data sets that meet functional / non-functional business requirements.
2. Built the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and GCP ‘big data’ technologies.
3. Built analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
4. Skilled with big data tools: Hadoop, Spark, Kafka.
5. Skilled with relational SQL and NoSQL databases, including Postgres and Cassandra.
6. Skilled with data pipeline and workflow management tools: Argo, and Airflow.
7. Skilled with GCP cloud services: Bigquery, Cloud Storage, GKE, Vertex AI Pipelines.

Experience

10+ Years

Level

Senior

Education

BSDS

Description :

1. Optimized and fine-tuned data pipelines for performance and scalability.
2. Implemented data quality checks and monitoring to ensure data accuracy and integrity in both the data warehouse and data lakehouse.
3. Developed and maintained data integration tools and frameworks to enable seamless data access for various stakeholders.
4. Worked on integrations with external services such as Jira, Splunk, Oracle, MES, ERP, and other third-party systems to facilitate data exchange and synchronization.
5. Led the design and development of data engineering framework that meets the needs of our internal processes and clients.
6. Developed and maintained software development best practices to ensure high-quality code, efficient testing, and timely releases.
7. Collaborated with cross-functional teams to understand data requirements, translate them into software solutions, prioritize features, and ensure our data engineering solution integrates seamlessly with other software products.

Experience

7-10 Years

Level

Management

Education

BSDS

Description :

1. Generated reports and insights from data to support decision-making processes.
2. Presented findings to stakeholders and explained technical concepts clearly and concisely.
3. Collaborated with cross-functional teams including data scientists, analysts, and business stakeholders.
4. Documented data processes, workflows, and data dictionaries for knowledge sharing and future reference.
5. Participated in team meetings, code reviews, and knowledge-sharing sessions.
6. Stayed updated with industry trends, best practices, and emerging technologies in data engineering.
7. Engaged in training programs, workshops, and online courses to enhance technical skills.

Experience

5-7 Years

Level

Executive

Education

Bachelor of Science in Computer Science

Description :

1. Worked with big data technologies such as Hadoop, Spark, and Kafka for large-scale data processing and analysis.
2. Implemented distributed computing techniques to handle massive datasets.
3. Performed data streaming and real-time data processing tasks.
4. Designed and implemented data models to represent business processes and data relationships.
5. Developed and maintained data warehouses and data marts for reporting and analytics purposes.
6. Conducted data profiling and data lineage analysis to understand data flow and usage.
7. Created visualizations and dashboards using tools like Tableau, Power BI, or other data visualization platforms.

Experience

2-5 Years

Level

Executive

Education

Bachelor of Science in Information Technology

Description :

1. Collaborated with data analysts and scientists to understand data requirements.
2. Developed and implemented data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses or data lakes.
3. Ensured data quality and integrity through data validation and cleaning processes.
4. Worked with APIs and data connectors to automate data collection processes.
5. Designed and optimized databases for efficient storage and retrieval of data.
6. Wrote and optimized SQL queries for data manipulation and analysis.
7. Monitored database performance and troubleshooting issues as they arise.

Experience

2-5 Years

Level

Executive

Education

Bachelor's Degree in Information Science

Description :

1. Responsible for designing and implementing a data warehouse using PostgreSQL to store and manage large datasets efficiently.
2. Responsible for developing ETL pipelines using Talend to extract data from various sources, transform it, and load it into the data warehouse.
3. Responsible for creating data models and performed data profiling to ensure data quality and integrity.
4. Responsible for utilizing Python and machine learning algorithms to analyze customer data and predict churn behavior.
5. Responsible for cleaning and preprocessing data using pandas and scikit-learn libraries.
6. Responsible for building and evaluating predictive models such as logistic regression and random forest for churn prediction.
7. Responsible for assisting in data collection, cleaning, and analysis for marketing campaigns using SQL queries and Excel.

Experience

7-10 Years

Level

Management

Education

Bachelor of Science in Information Technology

Description :

1. Contributed ideas and suggestions for process improvements and innovation in data management and analytics.
2. Ensured data privacy, security, and compliance with regulatory requirements (GDPR, HIPAA) in data handling and storage.
3. Implemented data encryption, access controls, and data masking techniques as needed.
4. Understood data modeling concepts and experience in designing data models to represent business entities, relationships, and processes. 
5. Expert with tools like ERD diagrams and data modeling software.
6. Expert with ETL processes including data extraction from various sources (APIs, databases, flat files), data transformation (cleaning, normalization, aggregation), and loading into data warehouses or data lakes.
7. Created and maintained optimal data pipeline architecture.

Experience

5-7 Years

Level

Executive

Education

Bachelor of Science in Information Technology

Description :

1. Presented data insights effectively to stakeholders.
2. Expert with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) for deploying and managing data infrastructure, storage, and computing resources in the cloud.
3. Understood data quality principles and practices to ensure data accuracy, completeness, consistency, and reliability. 
4. Collaborated effectively with data scientists, analysts, engineers, and business stakeholders. 
5. Explained technical concepts to non-technical audiences.
6. Participated in training programs, workshops, and self-study to enhance skills and knowledge.
7. Expert with data warehousing concepts, architectures (Kimball, Inmon), and tools (Snowflake, Redshift) for organizing and storing structured and unstructured data for reporting and analysis.

Experience

2-5 Years

Level

Executive

Education

Bachelor of Science in Information Technology

Description :

1. Supported project teams in developing client solutions that are passionate about exploratory data analysis.
2. Used knowledge of statistics, programming, data modeling, and advanced mathematics to recognize patterns, see opportunities, pose business questions, and make valuable discoveries that lead to better business decision-making.
3. Provided input on data visualization techniques and opportunities. 
4. Communicated findings clearly and succinctly to technical and non-technical audiences.
5. Identified, analyzed, and interpreted trends or patterns in complex data sets, including telemetry from storage arrays and other IT infrastructure components.
6. Used advanced machine learning techniques to predict or alert in case of failures, and recommend actions that can mitigate or resolve them.
7. Worked closely with our data science and analytics teams to ensure that data is readily accessible and usable for a variety of purposes, including reporting, machine learning, and business intelligence.

Experience

5-7 Years

Level

Executive

Education

Bachelor of Science in Information Technology

Description :

1. Interfaced with Engineers, Product Managers and Product Analysts to understand product goals and data needs.
2. Built data expertise and own data quality for allocated areas of ownership.
3. Managed data warehouse plans for a product or a group of products.
4. Supported critical data processes running in production.
5. Solved some of the most challenging business problems on the web and mobile Internet, at a scale that few companies can match.
6. Automated data collection, pre-processing and/or analysis.
7. Learned and adapted to new technologies, tools, and techniques in the rapidly evolving field of data engineering. 

Experience

2-5 Years

Level

Executive

Education

BSc Data Science