Data Science Engineer

Description

-Develop and maintain ETL pipelines using AWS Glue for data ingestion, transformation, and integration from various sources.

-Utilize Python and Spark for data preprocessing, feature engineering, and model development.

-Design and implement data lake architecture using Snowflake Data Lake, Snowflake data warehouse and S3 for scalable and efficient storage and processing of structured and unstructured data.

-Leverage SageMaker for model training, evaluation, deployment, and monitoring in production environments.

-Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop predictive models, and generate actionable insights.

-Conduct exploratory data analysis (EDA) and data visualization to communicate findings and trends effectively.

-Stay updated with advancements in machine learning algorithms, techniques, and best practices to enhance model performance and accuracy.

-Ensure data quality, integrity, and security throughout the data lifecycle by implementing robust data governance and compliance measures.

Responsibilities

• -Bachelor's degree or higher in Computer Science, Data Science, Statistics, or related field.
• -Proficiency in AWS services such as Glue, S3, SageMaker, and Snowflake Data Lake with 5-6 years of experience.
• -Strong programming skills in Python for data manipulation, analysis, and modeling.
• -Experience with distributed computing frameworks like Spark for big data processing.
• -Knowledge of machine learning concepts, algorithms, and tools for regression, classification, clustering, and recommendation systems.
• -Familiarity with data visualization tools with Tableau for creating meaningful visualizations.
• -Excellent problem-solving, analytical thinking, and communication skills.
• -Ability to work collaboratively in a team environment and manage multiple priorities effectively.
• -Experience deploying machine-learning models in production environments and monitoring their performance.
• -Knowledge of MLOps practices, model versioning, and automated model deployment pipelines.
• -Familiarity with SQL, NoSQL databases, and data warehousing concepts.
• -Strong understanding of cloud computing principles and architectures.
• -Certifications in AWS, Python, Spark, or related technologies.