AI/ML Data Engineer - Scalable Data Solutions

Placements24; Upington; Hybrid; ZAR 700,000 - 1,200,000; Full time

Benefits offered by this job

Competitive salary

Health benefits

Paid time off

Learning & development

Remote work

Cloud access

Job summary

Placements24 is seeking an AI/ML Data Engineer to design, build, and maintain scalable data solutions that power AI initiatives. You will develop robust data pipelines, ensure data quality, and optimize access for ML models and analytics.

This role is fully remote, offering collaboration with data scientists and software engineers. Ideal candidates have strong Python/Scala skills, SQL expertise, and hands-on experience with Spark/Hadoop and cloud data services.

Qualifications

• Bachelor's degree in CS/Engineering or related field.
• Proven data engineering experience building analytics/ML pipelines.
• Strong Python/Scala, SQL, and data modeling skills.

Responsibilities

• Design, build, and maintain scalable data pipelines for AI/ML apps.
• Develop ETL/ELT processes to ingest, transform, and load data.
• Ensure data quality, integrity, and availability for downstream use.
• Optimize data storage and processing for performance and cost.
• Implement data governance and security best practices.
• Collaborate with data scientists and ML engineers on data needs.
• Develop and manage data warehousing and data lake solutions.
• Monitor pipeline performance and troubleshoot issues.

Skills

SQL/NoSQL

Python

Scala

Big data

Data pipelines

Data governance

Cloud platforms

Education

Bachelor's degree in CS/Engineering

Tools

Apache Spark

Hadoop

Airflow

ETL tools

Job description

About the Role

Our client is seeking a skilled AI/ML Data Engineer to design, build, and maintain scalable data solutions that fuel their AI & Emerging Technologies initiatives. This role offers a flexible, fully remote work arrangement, allowing you to contribute from anywhere. You will be responsible for developing robust data pipelines, ensuring data quality, and optimizing data access for machine learning models and analytical applications. This is an excellent opportunity to work with large-scale datasets and cutting-edge technologies, playing a vital role in enabling advanced AI capabilities. You will collaborate closely with data scientists, ML engineers, and software developers to ensure seamless data flow and accessibility. The ideal candidate is proficient in data engineering best practices, possesses strong programming skills, and has a passion for building efficient and reliable data infrastructure to support AI innovation.

Key Responsibilities

• Design, build, and maintain scalable and robust data pipelines for AI/ML applications.
• Develop ETL/ELT processes to ingest, transform, and load data from various sources.
• Ensure data quality, integrity, and availability for downstream consumption.
• Optimize data storage and processing for performance and cost-efficiency.
• Implement data governance and security best practices.
• Collaborate with data scientists and ML engineers to understand their data requirements.
• Develop and manage data warehousing and data lake solutions.
• Monitor data pipeline performance and troubleshoot issues.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related quantitative field.
• Proven experience as a Data Engineer, with a focus on building data pipelines for analytics or ML.
• Proficiency in SQL and experience with relational and NoSQL databases.
• Strong programming skills in Python or Scala.
• Experience with big data technologies such as Apache Spark, Hadoop, or similar.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their data services.
• Knowledge of data warehousing concepts and ETL/ELT tools.
• Excellent problem-solving and analytical skills.
• Ability to work effectively in a remote team environment.

Benefits

• Competitive salary and potential for annual bonuses.
• Comprehensive health and wellness benefits package.
• Generous paid time off and holiday leave.
• Opportunities for continuous learning and professional development.
• Access to advanced data engineering tools and cloud environments.
• Fully remote work arrangement with flexibility.
• Collaborative and innovative work culture.