AWS Data Engineer

HuntWave Technologies; Pretoria; On-site; ZAR 600 000 - 800 000; Full time

Job summary

A leading technology company is seeking a Data Engineer to manage and optimize data infrastructure. The role involves designing scalable data architectures, developing ETL pipelines, and ensuring data security and compliance. Ideal candidates will have strong AWS and Python skills, with a focus on data engineering best practices. Join a dynamic team to drive innovation in data management and analytics.

Qualifications

• 5+ years working experience in Data Engineering.
• Strong skills in Python, especially PySpark for AWS Glue.
• Experience with AWS services for data warehousing.

Responsibilities

• Design and maintain scalable data architectures using AWS services.
• Develop and optimize ETL pipelines using AWS Glue and PySpark.
• Automate data workflows and ensure fault tolerance.

Skills

Python

Data Engineering

AWS

SQL

NoSQL

Education

Bachelors degree in Computer Science

Honors degree in Computer Science

Tools

AWS Glue

AWS S3

AWS Lambda

AWS Athena

PostgreSQL

MySQL

DynamoDB

Job description

Job Purpose:

Responsible for creating and managing the technological part of data infrastructure in every step of data flow. From configuring data sources to integrating analytical tools all these systems would be architected, built, and managed by a general-role data engineer.

Minimum education (essential):

Bachelors degree in Computer Science or Engineering (or similar)

Minimum education (desirable):

• Honors degree in Computer Science or Engineering (or similar)
• AWS Certified Data Engineer or
• AWS Certified Solutions Architect or
• AWS Certified Data Analyst

Minimum applicable experience (years):

5+ years working experience

Required nature of experience:

• Data Engineering development
• Experience with AWS services used for data warehousing, computing and transformations ie.AWS Glue (crawlers, jobs, triggers, and catalog), AWS S3, AWS Lambda, AWS Step Functions, AWS Athena and AWS CloudWatch
• Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, DynamoDB)
• Experience with SQL for querying and transformation of data

Skills and Knowledge (essential):

• Strong skills in Python (especially PySpark for AWS Glue)
• Strong knowledge of data modeling, schema design and database optimization
• Proficiency with AWS and infrastructure as code

Skills and Knowledge (desirable):

• Knowledge of SQL, Python, AWS serverless microservices,
• Deploying and managing ML models in production
• Version control (Git), unit testing and agile methodologies

Data Architecture and Management 20%

• Design and maintain scalable data architectures using AWS services for example, but not limited to, AWS S3, AWS Glue and AWS Athena.
• Implement data partitioning and cataloging strategies to enhance data organization and accessibility.
• Work with schema evolution and versioning to ensure data consistency.
• Develop and manage metadata repositories and data dictionaries.
• Assist and support with defining, setup and maintenance of data access roles and privileges.

Pipeline Development and ETL 30%

• Design, develop and optimize scalable ETL pipelines using batch and real-time processing frameworks (using AWS Glue and PySpark).
• Implement data extraction, transformation and loading processes from various structured and unstructured sources.
• Optimize ETL jobs for performance, cost efficiency and scalability.
• Develop and integrate APIs to ingest and export data between various source and target systems, ensuring seamless ETL workflows.
• Enable scalable deployment of ML models by integrating data pipelines with ML workflows.

Automation, Monitoring and Optimization 30%

• Automate data workflows and ensure they are fault tolerant and optimized.
• Implement logging, monitoring and alerting for data pipelines.
• Optimize ETL job performance by tuning configurations and analyzing resource usage.
• Optimize data storage solutions for performance, cost and scalability.
• Ensure the optimisation of AWS resources for scalability for data ingestion and outputs.
• Deploy machine learning models into productions using cloud based services like AWS Sagemaker.

Security, Compliance and Best Practices 10%

• Ensure API security, authentication and access control best practicespractises.
• Implement data encryption, access control and compliance with GDPR, HIPAA, SOC2 etc.
• Establish data governance policies, including access control and security best practises.

Development Team Mentorship and Collaboration 5%

• Work closely with data scientists, analysts and business teams to understand data needs.
• Collaborate with backend teams to integrate data pipelines into CI/CD.
• Assist with developmental leadership to the team through coaching, code reviews and mentorship.
• Ensure technological alignment with B2C division strategy supporting overarching client strategy and vision.
• Identify and encourage areas for growth and improvement within the team.

QMS and Compliance 5%

• Document data processes, transformations and architectural decisions.
• Maintain high standards of software quality within the team by adhering to good processes, practices and habits, including compliance to QMS system, and data and system security requirements.
• Ensure compliance to the established processes and standards for the development lifecycle, including but not limited to data archival.
• Drive compliance to the client's Quality Management System in line with the Quality Objectives, Quality Manual, and all processes related to the design, development and implementation of software related to medical devices.
• Comply to ISO, CE, FDA (and other) standards and requirements as is applicable to assigned products.
• Safeguard confidential information and data.