The presence of a Data Science Engineer in a Data Analytics team is pivotal for building and maintaining robust data infrastructure and scalable solutions. They develop and deploy data pipelines, design data architectures, and optimize systems for data processing and analysis, ensuring the reliability, efficiency, and scalability of the analytics platform to support the team’s data-driven initiatives.
Responsibilities:
- Design, develop, and maintain scalable data pipelines and ETL processes to support data analytics and reporting.
- Build and optimize data architectures for storage, retrieval, and processing of large datasets.
- Collaborate with data analysts and business analysts to understand data requirements and ensure data integrity and quality.
- Conduct exploratory data analysis to identify patterns, trends, and insights that drive business value.
- Implement and optimize algorithms for data mining, statistical analysis, and machine learning.
- Work closely with software engineers to integrate machine learning models into production systems.
- Implement data governance policies and procedures to ensure compliance and security.
- Evaluate and implement new technologies and tools to enhance data infrastructure and capabilities.
- Analyse and resolve issues related to data processing and performance.
- Document data pipelines, workflows, and technical specifications.
- Ensure the reliability, scalability, and performance of data infrastructure and analytical solutions.
- Stay ahead of with advancements in data science, machine learning, and big data technologies.
Requirements:
- Bachelor’s degree in computer science, Engineering, or related field.
- Shown experience of 8 + in data engineering, data science, or a related role.
- Strong programming skills in Python, Java, or another programming language commonly used in data science and engineering.
- Experience with technologies such as Hadoop, Spark, Kafka, or equivalent.
- Strong understanding of database systems and SQL, with experience working with relational and NoSQL databases.
- Solid understanding of machine learning algorithms and techniques
- Strong understanding of cloud platforms such as AWS, Azure, or Google Cloud.
- Display a keen eye for detail, strategically applying it where necessary to ensure precision and accuracy in all endeavors.
- Adopt a critical thinking approach, unafraid to pose questions and explore uncertain answers, driving continuous improvement and innovation.
- Demonstrate proactive problem-solving abilities, capable of independently identifying and resolving challenges. Exhibit curiosity, and share solutions and insights collaboratively to benefit the team.
- Ability to work effectively in a fast-paced and collaborative environment.
- Strong communication skills and the ability to present complex technical concepts to non-technical partners.
statistics Amazon Web Services (AWS) Apache Spark Apache Kafka Azure Machine-learning algorithms Communication Google Cloud Platform (GCP) data governance data-mining Machine Learning data-processing Hadoop Database engines Software Developer Data Analyst Data Science Python cloud-platforms Data Architect Big data SQL Java ETL NoSQL