Full Stack Data Engineer

A Day in Your Life at MKS:

Key Responsibilities:

• Design and Development: Design, develop, and maintain scalable and efficient data pipelines and architectures for data ingestion, processing, storage, and analysis.
• Data Integration: Integrate data from various sources, ensuring data consistency and quality.
• Database Management: Develop, optimize, and manage various database systems (both relational and NoSQL), ensuring high performance, availability, and security.
• Machine Learning Integrations: Collaborate with ML engineers to build data pipelines that feed and operationalize machine learning models, ensuring data quality and feature engineering for model training and inference.
• Data Transformation: Implement sophisticated data transformation, cleaning, and validation processes to prepare raw data for advanced analytics and machine learning applications.
• Front-End Development: Develop intuitive and interactive user interfaces and dashboards for data visualization, reporting, and exploration, making data accessible to a wide audience.
• Back-End Development: Build and maintain scalable APIs and services for secure and efficient data access, manipulation, and integration with other applications.
• Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
• Performance Optimization: Optimize data systems for performance, scalability, and reliability.
• Documentation: Maintain comprehensive documentation of data systems, processes, and workflows.
  

Required Skills:

• Programming Languages: Strong proficiency in Python is essential. Experience with Java or Scala is also highly valued.
• Database Technologies: Experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB).
• Machine Learning Fundamentals: Understanding of machine learning concepts, data preprocessing for ML, and experience with relevant Python libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Cloud Platforms: Hands-on experience with major cloud service providers (e.g., AWS, Azure, Google Cloud), including their data and compute services.
• Data Processing Frameworks: Knowledge of frameworks like Apache Spark, Hadoop, or Kafka.
• Front-End Technologies: Familiarity with front-end technologies such as HTML, CSS, JavaScript, and frameworks like React or Angular.
• APIs: Experience in building and consuming RESTful APIs.
• Data Visualization: Skills in data visualization tools like Tableau, Power BI, or D3.js.
• Problem-Solving: Strong analytical and problem-solving skills.
• Communication: Excellent communication and collaboration abilities.
  

Preferred Qualifications:

• Education: bachelor's or master's degree in computer science, Data Engineering, or a related field.
• Experience: Required minimum experience of 2 to 5 years of related experience in a similar role or in software engineering, data engineering, or full-stack development.
• Certifications: Relevant certifications in data engineering or cloud platforms.