Data Analytics Engineer

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Apply statistical techniques and data visualization tools to analyze data sets and extract meaningful insights.
• Interpret the results of data analysis and translate them into actionable recommendations for stakeholders.
• Develop and implement statistical\ML models and algorithms to solve specific business problems or make predictions.
• Create and maintain optimal data pipeline architecture and tools for internal process improvements which include automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Machine Learning Modeling: Develop, train, and evaluate machine learning models using algorithms such as regression, classification, clustering, and deep learning.
• Collaborate with cross-functional teams such as data engineers, business analysts, and decision-makers to understand requirements and deliver insights.
• Perform other duties as assigned.

Requirements

REQUIRED:

• Bachelor's Degree or higher in Computer Science, Statistics, Informatics, Information Systems, engineering or equivalent fields.
• At least 1 years of relevant experience.
• Data analysts need strong analytical skills to interpret complex data sets and extract meaningful insights. This includes the ability to identify trends, patterns, and correlations within the data.
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases and big data platforms, esp. MySQL, PostgreSQL and MSSQL.
• Proficiency in programming languages commonly used in data science such as Python. Additionally, proficiency in using data science tools and frameworks.

PREFERRED:

• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. Familiarity with Business Intelligence tools, Machine Learning concept and Big Data Platform is a plus.
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets. Hadoop, Amazon Redshift, MPP Presto, Hive, ActiveScale and S3 experience will be highly beneficial. Otherwise, passion to learn these new technologies is essential.
• Experience authoring, scheduling and monitoring the data workflows, esp. with Apache Airflow.
• Experience in Docker, the leading containerization platform, including building Docker images for data science applications, ensuring reproducibility and portability of environments. Managing Docker containers and orchestrating multi-container application.

SKILLS:

• Strong written and verbal communication in English
• Strong project management and organizational skills. Able to work and communicate well with cross-functional teams in a dynamic environment.